GEOFORT

TOWARDS THE BEST MEASURED PART

OF THE NETHERLANDS

GEOFORT

TOWARDS THE BEST MEASURED PART OF THE NETHERLANDS

Geomatics Synthesis Project 2007

D. AltundagA. Bantis

S.A. BrielsJ.F. Cruiming

B. van GoorN. Madentzoglou

R.A. MolijnS.C. Put

MSc GeomaticsDelft University of Technology

5 November 2007

Preface

This report presents a research project of two months, done by students in the secondyear of the MSc programme in Geomatics, Delft University of Technology. The aim of this‘Geomatics Synthesis Project’ - from an educational point of view - is to let the studentscombine the theory acquired in the first year of the MSc programme in a practical project.In this way, students get hands-on experience in project management and different aspectsof the working field of Geomatics.

The project was in close cooperation with Stichting GeoFort, led by Bart Bennis andWillemijn Simon van Leeuwen. The GeoFort will become an attraction with an educativecharacter, focused on the world of geo-information. The aim of the GeoFort will be tobecome the symbol and promotional center of geo-information. With the GeoFort, thevalue of geo-information will become more visible to the wide public.

The following companies participated in this ‘Geomatics’ part of the GeoFort project:Arcadis, Cyclomedia, Grontmij, Ingenieursbureau BCC, Ingenieursbureau Passe-PartoutBV, LNR Globalcom, Lumiad, and Miramap. These companies offered their services andexpertise to the project. In addition, Dr. Cliff Randell from the University of Bristoloffered a demonstration of his positioning system for this project. From Delft Universityof Technology advise and feedback was given by Edward Verbree, Frank Kleijer, TjeuLemmens, Kourosh Koshelham, and Theo Thijssen.

Due to all help from the companies, the advise from our supervisors from Delft Univer-sity of Technology, and the feedback of Bart and Willemijn, this project has been broughtto a successful end. The results and products presented in this report bring the GeoFortone step closer in becoming the best measured part of the Netherlands.

Delft, November 2007

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Contents

1 Introduction 11.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Aim of this project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3 Outline of the report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2 GeoFort - Description of the Fortress 3

3 Project Management 53.1 DSDM project approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

3.1.1 MoSCoW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53.1.2 Timeboxing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

3.2 Parties involved in the project . . . . . . . . . . . . . . . . . . . . . . . . . 6

4 3D Model of Historical Situation 84.1 Model requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84.2 Available data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84.3 Reconstruction methodology . . . . . . . . . . . . . . . . . . . . . . . . . . 10

4.3.1 Digitisation approaches . . . . . . . . . . . . . . . . . . . . . . . . . 104.3.2 Processing approach . . . . . . . . . . . . . . . . . . . . . . . . . . 114.3.3 Representation approaches . . . . . . . . . . . . . . . . . . . . . . . 124.3.4 Representation approach for historical model . . . . . . . . . . . . . 15

4.4 Resulting model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174.5 Quality assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

5 3D Model - Current Situation 275.1 Model requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

5.1.1 DTM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285.1.2 3D representation of buildings . . . . . . . . . . . . . . . . . . . . . 285.1.3 Other objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

5.2 Technique determination . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295.2.1 Acquisition techniques for the DTM . . . . . . . . . . . . . . . . . . 295.2.2 Acquisition techniques for modelling buildings . . . . . . . . . . . . 305.2.3 Technique trade-off . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

5.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315.3.1 AHN data specifications . . . . . . . . . . . . . . . . . . . . . . . . 315.3.2 RTK-GPS measurements . . . . . . . . . . . . . . . . . . . . . . . . 325.3.3 Software used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

5.4 Resulting model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

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5.5 Quality assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

6 Interior Model of the GeoFort 386.1 Model requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

6.1.1 Visualisation purposes . . . . . . . . . . . . . . . . . . . . . . . . . 396.1.2 Basis for tracking and tracing system . . . . . . . . . . . . . . . . . 406.1.3 Construction and design purposes . . . . . . . . . . . . . . . . . . . 406.1.4 Measurement and time constraints . . . . . . . . . . . . . . . . . . 40

6.2 Technique determination . . . . . . . . . . . . . . . . . . . . . . . . . . . . 406.2.1 Close-range photogrammetry . . . . . . . . . . . . . . . . . . . . . . 416.2.2 Tacheometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 436.2.3 Laserscanning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 456.2.4 Technique trade-off . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

6.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 486.3.1 Used instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . 486.3.2 Measurement setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 486.3.3 Modelling software . . . . . . . . . . . . . . . . . . . . . . . . . . . 496.3.4 Processing steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

6.4 Resulting model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 516.4.1 Laserscan model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 516.4.2 Vector model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 516.4.3 Texturised 3D interior model . . . . . . . . . . . . . . . . . . . . . 536.4.4 Data size of the models . . . . . . . . . . . . . . . . . . . . . . . . . 53

6.5 Quality assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 546.5.1 Tacheometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 546.5.2 Laserscanner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 556.5.3 Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 556.5.4 Accuracy of end result . . . . . . . . . . . . . . . . . . . . . . . . . 56

6.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

7 Design of a Real-Time Visitor Tracking and Tracing System 587.1 Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

7.1.1 Positioning system . . . . . . . . . . . . . . . . . . . . . . . . . . . 597.1.2 Handheld . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 607.1.3 Data transfer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 607.1.4 Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

7.2 Determination of positioning techniques . . . . . . . . . . . . . . . . . . . 617.2.1 Analysis aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 617.2.2 WiFi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 627.2.3 RFID . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 647.2.4 Ultrasonic positioning . . . . . . . . . . . . . . . . . . . . . . . . . 667.2.5 UWB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 677.2.6 Bluetooth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 697.2.7 GPS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 707.2.8 Technique trade-off . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

7.3 Market investigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 737.3.1 Positioning systems . . . . . . . . . . . . . . . . . . . . . . . . . . . 737.3.2 Software development - Geodan . . . . . . . . . . . . . . . . . . . . 75

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7.4 Pilot WiFi positioning system . . . . . . . . . . . . . . . . . . . . . . . . . 757.4.1 Preparation of the pilot . . . . . . . . . . . . . . . . . . . . . . . . 757.4.2 Data acquisition methodology . . . . . . . . . . . . . . . . . . . . . 787.4.3 Analysis methodology . . . . . . . . . . . . . . . . . . . . . . . . . 797.4.4 Pilot results and analysis . . . . . . . . . . . . . . . . . . . . . . . . 79

7.5 Pilot ultrasonic positioning system . . . . . . . . . . . . . . . . . . . . . . 867.5.1 Preparation of the pilot . . . . . . . . . . . . . . . . . . . . . . . . 867.5.2 Data acquisition methodology . . . . . . . . . . . . . . . . . . . . . 877.5.3 Analysis methodology . . . . . . . . . . . . . . . . . . . . . . . . . 887.5.4 Pilot results and analysis . . . . . . . . . . . . . . . . . . . . . . . . 89

7.6 Comparison between WiFi and ultrasonic positioning system . . . . . . . . 927.6.1 Comparison of APE and precision . . . . . . . . . . . . . . . . . . . 927.6.2 Comparison of the functionality . . . . . . . . . . . . . . . . . . . . 937.6.3 Comparison of costs . . . . . . . . . . . . . . . . . . . . . . . . . . 95

7.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

8 Conclusions 988.1 3D historical model of the GeoFort . . . . . . . . . . . . . . . . . . . . . . 988.2 3D model of the current situation . . . . . . . . . . . . . . . . . . . . . . . 988.3 3D model of the interior of the GeoFort . . . . . . . . . . . . . . . . . . . . 998.4 Design of a real-time visitor tracking and tracing system . . . . . . . . . . 998.5 This project as a basis for the GeoFort . . . . . . . . . . . . . . . . . . . . 100

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List of Figures

2.1 Location of the GeoFort. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.2 Aerial photograph of the GeoFort with the buildings indicated. . . . . . . . 4

3.1 Diagram showing the relations between the parties in the project. . . . . . 7

4.1 Requirements discovery tree for the 3D historical model. . . . . . . . . . . 94.2 Volume-based representations. . . . . . . . . . . . . . . . . . . . . . . . . . 134.3 Surface-based representations. . . . . . . . . . . . . . . . . . . . . . . . . . 144.4 Empty circumcircle principle of the Delaunay triangulation. . . . . . . . . 164.5 TIN of the GeoFort in 1885 and in 1904. . . . . . . . . . . . . . . . . . . . 164.6 Example of the addition of breaklines to the model and the adaption of the

TIN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174.7 Workflow showing the insertion of a 3D object in a 2.5D TIN. . . . . . . . 174.8 3D historical model of 1885. . . . . . . . . . . . . . . . . . . . . . . . . . . 194.9 3D historical model of 1904. . . . . . . . . . . . . . . . . . . . . . . . . . . 204.10 Comparison of the models of 1885 and 1904. . . . . . . . . . . . . . . . . . 214.11 Comparison of the models of 1904 and 2007. . . . . . . . . . . . . . . . . . 224.12 Orthophoto of the GeoFort with superimposed GPS-RTK and tacheometry

measurements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244.13 Orthophoto of the GeoFort, superimposed with digitisation lines. . . . . . . 25

5.1 Requirements discovery tree of the 3D model of the GeoFort. . . . . . . . . 285.2 Combination of AHN and RTK-GPS measurements. . . . . . . . . . . . . . 325.3 Visualisation of building models with texture mapping applied. . . . . . . . 335.4 Top-view of the current DTM of the GeoFort. . . . . . . . . . . . . . . . . 335.5 DTM of the current status of the GeoFort. . . . . . . . . . . . . . . . . . . 345.6 Final 3D model of the GeoFort. . . . . . . . . . . . . . . . . . . . . . . . . 345.7 Aerial photograph superimposed on the DTM as a texture. . . . . . . . . . 355.8 Distribution of selected check points. . . . . . . . . . . . . . . . . . . . . . 365.9 Histogram of height differences between the RTK-GPS measurements and

the AHN data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

6.1 Requirements discovery tree of the interior model of the GeoFort. . . . . . 396.2 The distances and angles measured by a tacheometer. . . . . . . . . . . . . 436.3 Modulation of the laser signal for the phase shift distance measurements. . 456.4 Laserscan model of building A. . . . . . . . . . . . . . . . . . . . . . . . . 516.5 Vector model of the ground floor of building A. . . . . . . . . . . . . . . . 526.6 2D map of building A, derived from the 3D vector model. . . . . . . . . . . 526.7 3D interior model with texture mapping applied. . . . . . . . . . . . . . . . 53

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7.1 Requirements discovery tree of the visitor tracking and tracing system. . . 597.3 2D map of the pilot test site, combined with grids on three of the nails. . . 777.4 Principle of the Ekahau positioning system. . . . . . . . . . . . . . . . . . 777.5 Measurement principle: example of the computed position at the location

of nail 113. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 787.6 The 2D map used for the corridor test. . . . . . . . . . . . . . . . . . . . . 797.7 Scatterplot of the computed positions and ‘true’ position of nail 110 in

northern direction, and plot of the deviations over time of nail 112 in easterndirection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

7.8 Boxplot of the deviations of nail 113 in northern direction. . . . . . . . . . 827.9 Plots showing the APE and precision of observations at nail 110. . . . . . . 827.10 Plots showing the APE and precision of observations at nail 112. . . . . . . 837.11 Plots showing the APE and precision of observations at nail 113. . . . . . . 837.12 Plots with the mean deviations of the positioning in the corridor. . . . . . 847.13 The mean deviations per orientation in the corridor. . . . . . . . . . . . . . 857.14 2D map of building A with an enlargement of the test site for the ultrasonic

positioning system. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 867.15 Example of an ultrasonic transducer and control unit. . . . . . . . . . . . . 877.16 The grid that was used for the measurements. . . . . . . . . . . . . . . . . 887.17 Scatterplots of the observations with the corresponding ‘true’ position of

the control points 1A, 5A, and 9A. . . . . . . . . . . . . . . . . . . . . . . 907.18 Scatterplots of the observations with the corresponding ‘true’ position of

the control points 1A, 5A and 9A, based on the offset corrected data. . . . 917.19 The presentation provided by the Ekahau Positioning Engine on the laptop. 947.20 The visualisation of the position provided by the ultrasonic positioning

system. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

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List of Tables

4.1 Comparison of volume representations. . . . . . . . . . . . . . . . . . . . . 134.2 Comparison of surface representations. . . . . . . . . . . . . . . . . . . . . 154.3 Transformation residuals between the orthophoto and the RTK-GPS and

tacheometry measurements. . . . . . . . . . . . . . . . . . . . . . . . . . . 244.4 Transformation residuals between the orthophoto and the map digitisation. 25

5.1 Trade-off study for the 3D model of the current situation of the GeoFort. . 31

6.1 Trade-off study for the 3D interior model of the GeoFort. . . . . . . . . . . 476.2 Specifications of laserscanner Leica HDS4500. . . . . . . . . . . . . . . . . 486.3 Specifications of tacheometer Trimble S6. . . . . . . . . . . . . . . . . . . . 496.4 Data statistics for the three different models. . . . . . . . . . . . . . . . . . 54

7.1 Trade-off study for positioning techniques. . . . . . . . . . . . . . . . . . . 727.2 APE and precision per orientation and per nail. . . . . . . . . . . . . . . . 807.3 APE and precision per orientation in the corridor. . . . . . . . . . . . . . . 857.4 Coordinates per grid point number. . . . . . . . . . . . . . . . . . . . . . . 897.5 APE and precision per control point and per height level. . . . . . . . . . . 907.6 APE and precision of the ultrasonic positioning system. . . . . . . . . . . . 917.7 Cost estimations per positioning system. . . . . . . . . . . . . . . . . . . . 96

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Summary

‘Stichting GeoFort’ has the ambition to promote geo-information in an appealing andspecial way to the wide public, by transforming an old fortress (‘Fort bij de NieuweSteeg’, near Leerdam, the Netherlands) into a knowledge and information centre for geo-information. This Geomatics Synthesis Project forms a basis for this ambition to becomereality. Four project goals are defined in this context that are treated in this report:

� 3D historical model of the GeoFort;

� 3D outdoor model of the current situation of the fortress;

� 3D model of the interior of the GeoFort;

� Design of a real-time visitor tracking and tracing system.

The 3D historical model is created for visualisation purposes and as a basis for furtherspatial analysis. The reconstruction was performed with the use of historical maps from1885 and 1904, which were made available from the ‘Menno van Coehoorn Stichting’.To create a 3D model from these images, they were digitised using CAD software. Thecreation of the 3D model was done with ArcGIS, which is capable of both visualisingand analysing 3D models. A TIN data structure was used for this model. Objects thatcontain multiple heights for each xy-pair were modelled using 3D faces (B-reps). Theaccuracy of the final product is strongly related to the accuracy of the data provided. Inthe absence of a reference dataset, an attempt has been made to assess the quality of themodel. The followed strategy included the comparison of the current model of the fortressto the orthophoto of 1940, and then a comparison between the othophoto of 1940 andthe model of 1904. The resulting errors were in the order of metres. The reason for themagnitude of the error is the accumulated effect of digitisation errors, natural changes ofthe fortress (e.g. erosion of the boundary of the island), and a non-uniform distributionof sample points.

The 3D model of the current situation of the fortress consisted of a Digital TerrainModel (DTM) and a 3D models of buildings. The DTM was created by combining Real-Time Kinematic (RTK) GPS and AHN (Actueel Hoogtebestand Nederland) laserscanningdata. Although there are some systematic shifts between these two datasets, an accurateDTM has been derived. The buildings have been modelled with tacheometry and RTK-GPS. Texture mapping has been applied to give the buildings a more realistic appearance.The combination of the DTM and the model of buildings and other objects provides a basisfor many applications, such as visualisations, making comparisons between the historicalstatus and the current status of the fortress, and using it for planning and constructionpurposes.

For the interior model of the GeoFort, tacheometry was chosen as the most suitablemeasurement technique. In combination with pen computer technology this technique

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allows a very short processing time to create an accurate vector model with a low amountof detail. To add more detail to this model, point coordinates from laserscanning datawere added. Tacheometric data was processed using the Microstation software package,points from the laserscans were added using the Cyclone software package. Surfaces weremodelled using SketchUp. This software was also used to add photographic textures.Four models have been created. A point cloud model gives the most detailed and accurategeometric representation of the actual situation. A 3D vector model provides accuratedimensions of the building which can serve for construction and interior design purposes.A 2D map is created and used as a basis for the positioning system pilots of the real-timetracking and tracing system. A 3D model with faces and mapped textures is created touse for visualisations to the public.

The design of the tracking and tracing system started with a determination of therequirements for the positioning system, the data transfer network, and the handheldreceiver and its interface, followed by a comparison of positioning techniques. From this,two positioning techniques were selected: WiFi positioning and ultrasonic positioning. Forboth systems a pilot has been executed to assess the quality and reliability of the systems.The accuracy of WiFi positioning was in the order of metres, while the accuracy of theultrasonic positioning system was in the order of centimetres. It was concluded that WiFipositioning is currently the best solution for indoor positioning at the GeoFort, augmentedby GNSS for outdoor positioning and ultrasonic positioning for indoor locations where ahigher accuracy is needed than is currently possible with WiFi positioning.

The results of these four project parts provide the GeoFort with a basis for theirattractions and exhibitions. In addition, the models created in this project can serve asa tool for further analysis, or, for example, for construction and design purposes.

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Chapter 1

Introduction

1.1 Motivation

Geo-information is the basis of many decisions. This importance has been recognisedby people who are directly involved in the field of geo-information, but not yet by thewide public. This results in a shortage of people and their expertise in this workingfield. ‘Stichting GeoFort’ has the ambition to promote geo-information in an appealingand special way, by transforming an old fortress into a knowledge and information centrefor geo-information. The aim of this ‘GeoFort’ will be to promote the aspects of geo-information to a wide range of visitors, from young children to adults with differentbackgrounds and interests. The ambition of the GeoFort is to become the best measuredpart of the Netherlands. This Geomatics Synthesis Project forms a basis for this ambitionto become reality.

1.2 Aim of this project

The aim of the GeoFort project is to provide an interactive ‘geo-information experience’to visitors of the GeoFort. The basis for GeoFort is geo-information. Many attractions ofthe GeoFort will be based on three-dimensional (3D) models of the interior and exteriorof the GeoFort; of present and past situations. Furthermore, an interactive tour will beprovided to the visitors, based on a real-time visitor tracking and tracing system. Basedon the aim of GeoFort, four project goals are defined:

� 3D historical model of the GeoFort;

� 3D model of the current situation of the fortress;

� 3D model of the interior of the GeoFort;

� Design of a real-time visitor tracking and tracing system.

1

The link between these four project parts can be illustrated with the following example:

Imagine a visitor of the GeoFort. At arrival he receives a small handheld computer with colourdisplay and earphones. He enters some personal information, and the handheld automaticallydetermines a tour for the visitor that suits him best, based on his interests. He starts the tourwith a view of the entire fortress in 3D on his handheld. The building to where he is guided ishighlighted on the map. In front of the building, he listens to a story about how the GeoFortlooked like in 1940. On the display of his handheld, he sees a 3D model of the state of thebuilding in 1940.He enters the building. The tour guides him through the corridor and other parts of the building,and the position of the man in shown in real-time on the handheld. After a while he is guided tothe left in a building, but he actually decides to go right where he has seen something that hascaught his interest. The tour is automatically adjusted to his change of plans.When the tour is ended after a certain time, the man has experienced an interactive, informativetour throughout the GeoFort.

The GeoFort will combine many aspects of geo-information to the visitors, such as dataacquisition, 3D modelling, positioning, Location-Based Services (LBS), and cartography.The project goals as defined above will provide the basis for the further development ofthe GeoFort.

1.3 Outline of the report

The report will follow a structure based on the four project goals. First, in Chapter 2, ashort overview of the history of the GeoFort will be given. This is followed in Chapter 3by a description of the project management principles used in this project. Then, inChapter 4, the 3D historical model of the GeoFort will be presented. This model representsthe situation at the time the fortress was still operational. The 3D model of the presentsituation of the GeoFort is presented in Chapter 5. After that, the model of the interiorof the GeoFort is discussed in Chapter 6. The design of the visitor tracking and tracingsystem is presented in Chapter 7. The report concludes with an overall evaluation anddiscussion of the results.

2

Chapter 2

GeoFort - Description of the Fortress

In the nineteenth century, a Dutch defence system has been developed, the ‘Nieuwe Hol-landse Waterlinie’. This system consisted of a line of fortresses going from north to south.The total length of the Nieuwe Hollandse Waterlinie is 85 km. The defence strategy ofthis line of fortresses was that, in times of war, the area between the fortresses could beflooded to create a physical barrier for the enemy of several kilometres width. The waterlevel was between 30 cm and 60 cm; too deep for infantry and too shallow for normalmarine vessels. The Nieuwe Hollandse Waterlinie fulfilled this defence task from 1815 to1940. It was actually flooded in the Franco-Prussian War (1870), during World War I(1914-1918), and at the beginning of World War II (1939) [42]. When aircraft came inuse in the twentieth century, the defence task of the Nieuwe Hollandse Waterlinie becameobsolete.

One of the fortresses of the Nieuwe Hollandse Waterlinie is GeoFort, previously knowas ‘Fort bij de Nieuwe Steeg’. It was built in 1878 to close of a meander in the river Linge.It also had to support the neighbouring fortress ‘Fort Asperen’ [40]. GeoFort is locatedin Herwijnen, near Leerdam in the centre of the Netherlands (Figure 2.1). The fortresswas designed to accommodate a complete garrison of 340 men. An aerial photograph ofGeoFort is presented in Figure 2.2. The fortress consists of several buildings [36] [51]:

Building A was used by the officers. They had their own rooms: the ‘officierswacht’ and‘wachtlokaal’. From this building they commanded their troops. Apart from theofficers rooms, which were on the top level, the building also had rooms for storingammunition and projectiles, as well as a room where the laundry was done by thetwo laundry women. In the building there are two ‘vuurmonden’: strategic spotsfrom which the fortress could be defended against enemies.

Building B is the main building, located in the centre of the fortress. The commandingofficer had his office there, the ‘bureel commandant’. Furthermore, the building wasused as a hospital and pharmacy and there were sleeping quarters for the troops.In this building also the kitchen with food supply rooms were located, as well ascells for locking up arrested people. On the east side, in the direction of buildingA, there were also rooms for storage of ammunition and projectiles.

Building C, D, E, F and G primarily provided storage space for ammunition and pro-jectiles. They also had ‘vuurmonden’ for defending the fortress. In building D alsoexplosives were stored, which were used by the military engineers. In building Gthere are a number of guard rooms for four men each.

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‘Artillerieloods’: used to store ammunition, projectiles and other military goods.

‘Wachterswoning’: this is a small house with four rooms at the south of the fortress.From this house the bridge was guarded.

In the future these buildings will be used for exhibitions, conferences, and training pro-grammes, related to geo-information.

Figure 2.1: Location of the GeoFort [22].

Figure 2.2: Aerial photograph of the GeoFort with the buildings indicated. [21].

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Chapter 3

Project Management

There was limited time available to achieve the four goals as outlined in the introduction.From the start of the project to the presentation of the final results, only eight weeks areavailable. Not only due to this limited time frame, but also due to the different partiesinvolved in the project, structured project management was an important aspect to makethe project a success. This chapter describes the concepts of project management thatwere used.

3.1 DSDM project approach

In traditional project management the functionality of the system or product to be de-veloped in the project is fixed, and the number of people working on the project andthe amount of time available is variable. In the Dynamic System Development Method(DSDM), the number of people and the available time are fixed and the functionality isvariable. This is in accordance with the situation for this project. Another importantproperty of DSDM is the involvement of the users in the development [29]. ‘Users’ isreferring to the people who - in the end - will be using the developed product. Communi-cation with the users will lead to a solution that will fit them best. The development ofthe product is an iterative process, in which functionality is added step-by-step and everystep is evaluated by the user. Products are delivered during the project; not only at theend of the project. In this way the user is highly involved in the end result.

3.1.1 MoSCoW

An important concept in DSDM is MoSCoW. MoSCoW is used for prioritisation of therequirements. All the requirements are categorised as a ‘Must have’, ‘Should have’, ‘Couldhave’ or a ‘Would like to have’. Due to the limited time frame, the ‘Must haves’ areimplemented first. According to the DSDM consortium [14], implementation of these twofirst categories often delivers 80 percent of the total product. This is referred to as the80/20 rule (20 percent of the requirements deliver 80 percent of the product).

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MoSCoW for this project is specified as:

Must haves :

� 3D model of the historical situation of the GeoFort;

� 3D model of the current situation of the fortress;

� 3D model of the remises A1 and A2 and the corridor that connects these remises;

� System design for xyz-coordinates of tracking and tracing data.

Should haves :

� Interactive photo-realistic 3D website of fortress (historical situation, current situa-tion, and interior);

� 3D animation of a transition from the historical model into the current situation;

� Pilot of tracking system in remises A1 and A2 and the corridor that connects theseremises.

Could haves :

� Website containing a 2D map of the GeoFort with real-time visitor tracking andtracing data visible;

� Design of a user interface for the handhelds the visitors will carry in the GeoFort.

Would haves :

� Provide location-based information on the handheld of the visitor.

3.1.2 Timeboxing

Timeboxing is the concept of assigning a fixed time to the realisation or developmentof a product. The milestones, specifying when a certain (part of the) product shouldbe finished, are immovable. This makes it necessary to focus on the most importantrequirements of the user (the ‘Must haves’) during the project.

3.2 Parties involved in the project

A key aspect in the GeoFort project is the participation of different parties, that can bedivided in two groups: the core parties and the parties who are not directly related to thisproject, but to the GeoFort project in general. The relations between these parties canbe visualised in a diagram (Figure 3.1), showing the parties involved and their relation tothe ‘geoproducts’ [31]. The core parties are Delft University of Technology (TU Delft),the GeoFort, the geo-companies offering their services in this project, and the end-users,being the future visitors of the GeoFort. Their relations can be summarised as follows:

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� TU Delft → geoproducts: development and delivering of geoproducts;

� Geoproducts → TU Delft: research results of project and learning process for thestudents in the project;

� GeoFort → geoproducts: feedback on product development;

� Geoproducts → GeoFort: basis for further development of GeoFort;

� Geo-companies → geoproducts: support on product development;

� Geoproducts→ geo-companies: publicity, name attached to GeoFort as a prestigiousproject;

� Visitors → geoproducts: determination of requirements of geoproducts;

� Geoproducts → visitors: basis for GeoFort that meets requirements of future visi-tors.

Figure 3.1: Diagram showing the relations between the parties in the project. The four core partiesdirectly influence the geoproducts, that are developed in this project.

Next to these core parties, four additional parties are involved. The people livingnearby are interested in the development of the GeoFort. Staatsbosbeheer, as part of theDutch government, is the owner of the fortress. Monumentenzorg is a foundation thatis interested in maintaining the GeoFort as a monument. The geo-community is closelywatching this project, as GeoFort will become a centre of geo-information, promotinggeo-information to their own benefit.

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Chapter 4

3D Model of Historical Situation

There are many ways to give an impression on the history of the GeoFort. It could bewritten as text, visualised in drawings or maps, or visualised as a 3D model. The advan-tages of a 3D model instead of a 2D map is that it will give a more realistic impression,because the heights are visualised. Another advantage of having a 3D model is that it canbe used for analysis, because 3D features are stored (with xyz-coordinates). In the pastalso maquettes were created as 3D models, but these were only used to give an impres-sion of the appearance of an object or building. Currently, a 3D model has many moreapplications.

Based on the added value of such a 3D model, the historical state of the GeoFort isreconstructed into a 3D model. The largest challenge for this project goal is to create amodel of something that is not there anymore. This means that no new measurementscan be taken to create this model. Therefore, the largest restriction on creating this modelis the available data.

After a description of the model requirements in Section 4.1, an overview of the avail-able data sources will be given in Section 4.2. Then, in Section 4.3, the reconstructionmethodology is discussed. After presenting the results in Section 4.4, the quality of themodel will be assessed, followed by concluding remarks about this part of the project.

4.1 Model requirements

The model must represent the historical state of the GeoFort, including buildings andenvironment. The extend of the model is based on the parcel boundary of the GeoFort.As the available data is the largest restriction, the accuracy considered for this modelshould be as accurate as possible with the historical maps provided. If models of differenttime periods are created, a comparison between these models could show the changes overtime.

Figure 4.1 shows the requirements discovery tree of the 3D historical model.

4.2 Available data

Building a historical impression of GeoFort at different time periods poses some limita-tions. Since no additional measurements can be made, one has to rely on historical mapsand documents. One problem of this approach is the heterogeneous nature of the data.

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Figure 4.1: Requirements discovery tree for the 3D historical model.

The available maps and photographs originate from different sources and have differentspecifications in terms of accuracy and intended use.

The data are obtained mainly from the ‘Menno van Coehoorn Stichting’ [36]. This isa foundation of volunteers with the overall purpose of documentation and maintenance ofhistorical military buildings and infrastructure in the Netherlands, as well as monumentsbuilt outside the country. The foundation collects data from different sources, such as theKadaster and municipalities, and makes inventories of:

� Books, magazines and other documents concerning buildings or fortresses;

� Unique drawings, ground plans, and maps;

� Photographs and slide collections.

Other sources are architectural designs of the fortress provided by architects as well asartistic impressions as designed by illustrators. Moreover, Staatsbosbeheer provided docu-ments on the historical landscape development of the ‘Fort bij de Nieuwe Steeg’ (GeoFort)and its surroundings. The available datasets that can be used for the reconstruction are:

� Top-view of the fortress as it was originally designed. The map also contains theheights of the buildings. The estimated date of map’s creation is between 1882 and1885 and the scale is 1:1000;

� Top-view of the fortress as it was actually built, including heights. This map isdrawn based on real measurements in the field. The map dates around 1904-1905with scale 1:1000;

� Technical drawings of the buildings of the GeoFort, containing foundations and sideand front sections;

� Orthophotos of the area dating around 1940;

� Historical topographic maps of multiple dates;

� Artistic illustrations of the fortress, designed by architects.

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For the historical reconstruction of the fortress it is decided to make use of the top-viewmaps, since it is the only source of information that indicates the state of the fortress atdifferent time periods. An orthophoto was available of 1940, but since it is not possibleto derive height information from only one orthophoto, a reconstruction of the fortressin 1940 was not possible. As far as individual buildings are concerned, the 3D model isbased on technical drawings. They provide sufficient information about the dimensions,heights and slopes.

4.3 Reconstruction methodology

The basis for the reconstruction of the historical situation of GeoFort at different timeperiods are the maps and photographs provided. These data, together with additionalinformation in the form of descriptions and explanations provided by experts (historiansand architects), are used to achieve a reliable overview of the different states of the fortress.

Since the data are available in analogue format only, the first step is to transform theminto a structured digital form. This can be done using digitisation, which is the processof converting analogue data into a digital format using a trace methodology.

The following section describes digitisation and representation approaches that canbe used. From these, the most suitable approach for the reconstruction of the model isselected, thereby taking into account the time constraint of this project.

4.3.1 Digitisation approaches

The analogue data can be digitised using one of the following two methods [16]:

� Manual digitisation (using a digitising table, ‘heads-up’ digitising);

� Automatic digitisation using scanning (semi-automatic line following, automaticraster scanning, and vectorisation).

Digitising can be performed with GIS software, if the software supports such oper-ations, or in a specialised computer-aided environment (such as AutoCAD). The mapshave to be georeferenced (i.e. points on the map are then referring to positions on theEarth’s surface) by means of ground control points.

Manual digitisation

Manual digitisation can be performed using the original paper map and a digitising table,or digitise directly from the computer screen (also known as heads-up digitising). In eithercase, the procedure involves the manual tracing of map features such as points, lines, andpolygons with a mouse or puck, thus allowing the coordinates of each point to be storedon the computer.

The first method involves an operator and a digitising table. The paper map is tapedto a digitising table and the operator uses a special mouse, called a cursor or a puck, totrace and record the features of the map. The table is composed of an electronic gridof wires that enables the position of the cursor to be recorded in xy-coordinates. Thecoordinates can refer to a plane, or to any geographical coordinate system. The finalaccuracy of the resulting vector map is a combination of the accuracy of recording andthe accuracy of the paper map.

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In heads-up digitising, the digital image of the map is projected on the screen, and theoperator traces the features of the map with the mouse. The process is prone to humanerrors, but for small-scale projects the quality of the digitised product can be acceptable.

Automatic digitisation

Fully automated digitisation is most commonly used in projects where the input dataquantity is large. It involves four steps [16]:

� Scanning of the map;

� Noise removal;

� Detection, binarisation, and skeletonisation of contours;

� Vectorisation of contours.

Usually the scanned image will contain many defects of the original map and mistakesin areas where the map detail is complex. Therefore, a treatment stage is required beforethe automatic vectorisation of contours. This includes noise removal (i.e. filtering),detection of contours for discontinuities, binarisation for segmenting the map into contourand background pixels (with gradient filtering), and skeletonisation for extracting theexact position of the edges.

In semi-automatic digitisation, a large part of line-following is controlled by softwareor hardware. This technique requires a human operator to control and assist the softwarein resolving anomalies or identifying features.

In principle, automatic raster-to-vector conversions perform similar tasks as manualdigitising using a digitising table. However, the scanning step deletes much of the humanintervention and produces better quality vector maps.

4.3.2 Processing approach

Most of the historical maps are more than 100 years old and consequently they containmany defects, such as line discontinuities. This prevents the use of automated digitisationapproaches, since they require a certain level of image quality to be able to apply patternrecognition techniques. Moreover, subsequent interactive operations will be very time-consuming since the source documents are both imperfect in terms of mapping precisionof the details shown, and inadequate in terms of resolution of information.

In the absence of a digitising table and given the availability of the original papermaps, a logical approach is the use of the heads-up digitising method. This allows forthe creation of new contour lines according to the slopes and heights. Moreover, thesmall scale of the project leads to a minimal number of human errors. Nevertheless, somedigitising errors, e.g. mismatches between the map and the digitisation, will always occur.

The software used is AutoCAD [6], which has proven its efficiency in the industryand design sector. Among its advantages are easy and fast designing, as well as detailedediting. Moreover, the output format of the software is an industry inherited standard,interoperable with many commercial GIS software such as ArcGIS [17].

Normally, scanning of the map is performed prior to the digitisation step, so that theanalogue map can be converted to a digital form without losing its geometric rigidity.

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However, in this case there was no access to the source documents, which made scanningimpossible. Instead, the maps were provided as digital photographs.

The image is imported into AutoCAD and converted to the scale as indicated on themap. This process results in a vector map containing the outline of the features and thecontour lines. This approach is applied for the reconstruction of the fortress as it was in1885 and in 1904, for which maps are available.

This approach is not applied for the state of the fortress in 1940, as there are no mapsavailable from that time period. Instead, a stereo-pair of photographs and an orthophotowas provided. One option is to use photogrammetric techniques to derive a 3D modelautomatically from the stereo-pair, but since the resolution of the photographs is verypoor, and information about the camera characteristics is absent, it is decided not to beused.

4.3.3 Representation approaches

A model is considered to be an abstraction of reality. However, it is not possible to modelall aspects of the real world due to the high complexity of real-world objects. Differentscientists choose to focus on different aspects and thus, a model is created to serve aspecific purpose. Which model or representation is used depends on the type of spatialdata and the intended application. Spatial representations differ in:

� How 3D space is represented;

� How data are stored and logically interrelated (data modelling);

� How the model is displayed.

The decision for using a certain representation approach depends mainly on the purpose ofthe model and the user’s needs. The reconstruction of the historical situation of GeoForthas to serve two purposes. First, it has to give an accurate impression of the fortress’historical state for visualisations to the visitor. Second, it has to be able to provide‘added value’ to specialists, such as historians. The first goal can be achieved by choosinga data model that supports appealing visualisations and the second by choosing a modelthat supports complex spatial operations. Among other criteria on which the choice fora specific spatial representation can be based are: complexity of the phenomenon, thecharacteristics of data, and storage volume. Two main classes of spatial representationscan be distinguished: volume-based and surface-based.

Volume representations

Volume representations essentially model the interior of an object (Figure 4.2). Thesetechniques fill the space completely with elementary objects. A distinction is made be-tween 3D arrays, octrees, Constructive Solid Geometry (CSG), and 3D Triangulated Ir-regular Network (3D TIN).

A 3D array is one of the simplest ways to represent 3D objects. All voxels (3Dequivalent of a pixel, i.e. 3D data elements) have the same size and each one occupiesthe same amount of computer memory. The disadvantage of this data structure is that itneeds a large amount of computing power and memory space.

An octree is a tree-like data structure mostly used to partition a 3D space by subdivid-ing it into eight octants. In an octree representation, objects are created of 2 × 2 × 2 =

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8 voxels. If all voxels within the object have the same value, then the object is stored asone element. If the voxels within the object do not have the same value, it will be sub-divided into eight elements and the process continues on a lower level. So, voxels with asimilar value will be merged if they are in the same cubic element. This has the advantageof data reduction, however, the disadvantage of this technique is that it will give a muchcoarser approximation of the surface than in the case of the 3D array approach, becausethe surface is represented with small cubes.

In general, voxel-based representation methods are simple and easy to maintain. Theyhave been used to model continuous phenomena (e.g. geologic features) and objects wherevolumetric computations are necessary. However, the technique produces a lot of dataand the overall precision is relatively low.

Figure 4.2: Volume-based representations (after [1]).

Table 4.1: Comparison of volume representations. A ‘+’ means that the criteria is an advantage of therepresentation, a ‘+/-’ means that it is neither an advantage nor a disadvantage and a ‘-’ means that thecriteria is a disadvantage of the representation.

Volume representation Vis

ualis

atio

n

Spat

ialop

erat

ions

Ter

rain

adap

tabi

lity

Dat

ast

orag

e

Reg

ular

/ir

regu

lar

obje

cts

3D array - +/- - - Regular

Octree - + - - Regular

CSG - +/- - + Regular

3D TIN + + + +/- Regular / Irregular

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Constructive Solid Geometry (CSG) uses solid primitives for representation, such ascubes, cylinders, and spheres, each one having a variety of parameters such as height,width, or radius. This technique is widely adopted in computer graphics, CAD and man-ufacturing industry where solid primitives are mathematically described by parameters.This makes the computation of volumes easy. The properties of the primitives and therelationships between them (Boolean operators) are described by a CSG-tree. However,the relationships between objects can get very complex, resulting in long computationtimes.

The 3D TIN (also known as TEtrahedral Network, TEN) approach is characterised byconnected, non-overlapping tetrahedrons. Each one is made of four nodes, six edges andfour faces. Like its 2D counterpart, TEN has many advantages in terms of rapid display,manipulation, and analysis. However, commercial software packages that can handle theTEN data structure are not yet commonly available.

Table 4.1 gives a summary of important volume representation characteristics in rela-tion with the project requirements.

Surface representations

Surface representations are used for modelling only the surface of an object. The ob-jects are represented by surface primitives. A distinction is made between grids, facetrepresentations and boundary representations (Figure 4.3).

A boundary representation (B-rep) defines an object in terms of the geometry of itsbounding surface. This representation uses low-level primitives such as points (0D), lines(1D), surfaces (2D), and solids (3D) to represent real-world objects. B-rep is appropriatefor representing real-world objects, because only visible properties are present in therepresentation. The disadvantages are that boundary representations are not unique andconstraints may get very complex [57].

The grid data structure is used extensively in most GIS systems for representingsurfaces. At each xy-location of the grid, the z-value is stored. The structure presentsmany advantages in terms of generation simplicity and topological information [1]. Onemajor drawback of this structure is that it is 2.5D, i.e. objects with multiple z-values (e.g.bridges, building arcs) cannot be represented.

Figure 4.3: Surface-based representations (after [1]).

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Table 4.2: Comparison of surface representations. A ‘+’ means that this criteria is an advantage of therepresentation, a ‘+/-’ means that it is neither an advantage nor a disadvantage and a ‘-’ means that thiscriteria is a disadvantage of the representation.

Surface representation Vis

ualis

atio

n

Spat

ialop

erat

ions

Ter

rain

adap

tabi

lity

Dat

ast

orag

e

Reg

ular

/ir

regu

lar

obje

cts

Grid - + - + Regular

Facet +/- + + +/- Irregular

B-rep + - + - Regular (planar)

In case of a facet representation the object’s surface is described by planar faces withvarying size and shape. One of the most known data modes that use planar triangles is theTriangular Irregular Network (TIN). In this case, the surface is described as a network oftriangular faces, each one consisting of 3 vertices holding the xyz-coordinate information[1]. This method is well known and used widely (along with grid data structure) for therepresentation of surfaces in a GIS.

Table 4.2 summarises the characteristics of each surface representation in relation withthe project requirements.

4.3.4 Representation approach for historical model

The complex nature of the fortress leads to the implementation of two different represen-tation strategies. Although a 3D TEN appears to be the best solution, using a volumerepresentation to represent the surface of GeoFort leads to unwanted data storage volumewhich could have implications during rendering of the model. Therefore a TIN data modelis used to represent the surface of the fortress. This structure is widely used in terrainmodelling applications because of its structural stability and terrain surface adaptability.Using a TIN data model, the sampling process can be adjusted according to the com-plexity of the surface, allowing zones of high or intense relief to be sampled more densely.Consequently, features such as steep slopes, ridges, and peaks can be represented better.Additional advantages are data interpolation simplicity and good object visualisation [1].

A TIN data model is composed of nodes, edges, and triangles. Each node stores thecoordinates of the corresponding point, allowing direct access to them. The points areconnected with a Delaunay triangulation, which uses the empty circumcircle criterion(Figure 4.4). This criterion states that in a plane of a set of points P the triangulationis such that no other point lies in the circumcircle of each triangle in that plane. Thisempty-circle criterion optimises for well-formed triangles and the resulting triangulationmaximises the smallest angle of the triangles. [53]. The resulting triangular faces describethe behaviour of the surface in terms of slope, surface length and aspect.

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Figure 4.4: Empty circumcircle principle of the Delaunay triangulation (source: [53]).

Figure 4.5: TIN of the GeoFort in 1885 (left) and in 1904 (right).

The TIN is constrained by using breaklines, which are constant lines that representabrupt surface changes. In this way, the non-overlapping triangles are prevented fromcrossing the breaklines, thereby obtaining a more realistic result. ArcGIS is used for thisprocess.

The above strategy has been applied for the representation of the topography of thesurface area of GeoFort. The resulting model is a 2.5D surface (Figure 4.5), meaningthat for each xy-coordinate pair only one z-value is present in the model. Although thisapproach is sufficient for representing surfaces, this is not true for real 3D buildings (i.e.objects that contain multiple z-values for each xy-coordinate pair). This process wasrepeated for the 1885 and 1904 case.

Objects that are on or above the surface are modelled separately using B-reps. Sincethese buildings can be considered as regular objects with well-defined dimensions, asdescribed in the technical drawings, they can be modelled by using regular 3D faces. Theprocess is carried out with the use of AutoCAD. These objects are mainly entrances tobuildings that contain vertical walls.

A problem one encounters when adding a ‘real’ 3D object (as opposed to a 2.5D object)in a TIN is that the relation between the object and the TIN will be either disjointor an intersection. This is the case when using the current industry formats (ArcGIS,AutoCAD). To avoid this and create an actual ‘meet’ between the two features, the TINhas to be adapted according to the boundary of the 3D object. Therefore, AutoCAD wasused to add additional breaklines to the model. These breaklines will adapt the currentTIN in ArcGIS. In Figure 4.6(a) it can be seen that two blue lines were added (whitearrows). The first breakline (indicated with the white arrow on the top of Figure 4.6(a))is added on the top of the 3D object, while the second breakline (indicated by the otherwhite arrow) is placed below the 3D object. The rest of the blue lines are already present

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in the current TIN. Since these two new lines were added, the shape of the TIN changes.It now meets the object instead of intersecting it. This can be seen in Figure 4.6(b),illustrating the bottom of the TIN with the 3D object. The TIN is represented in greenand it can be seen that it will not intersect the 3D object. Figure 4.7 gives an overviewof the process of inserting a 3D object into a TIN. Since incorporation of real 3D objectson the TIN is not possible (due to the differences in the data structure), the 3D objectsare added as a separate layer in ArcGIS.

(a) Addition of breaklines to themodel.

(b) Adapting the TIN to the breaklines.

Figure 4.6: Example of the addition of breaklines to the model and the adaption of the TIN.

Figure 4.7: Workflow showing the insertion of a 3D object in a 2.5D TIN.

4.4 Resulting model

The digitisation process resulted in a vectorised shape with heights as attributes. Thisvectorised shape together with the height attributes are used to create a TIN representingthe topography of the terrain surface for two time periods. The first TIN represents thesituation as the fortress was designed in the year 1882. The second TIN represents thesituation when the fortress was actually built, based on the design maps, in the year1904. The only information available for obtaining an impression of the fortress as it wasin 1940, was an orthophoto. Due to this limited data available, the 3D reconstruction of

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the fortress was not possible, because it is not possible to derive accurate height valuesfrom an orthophoto.

In some cases, additional breaklines had to be added to the TINs to generate therelief of the terrain more realistic. The resulting models were georeferenced to groundcoordinates by means of ground control points as provided by GPS. The resulting repre-sentation is a 2.5D surface, which is unable to handle the vertical walls of the entranceof some buildings. Therefore, these buildings were reconstructed separately, using CADsoftware. After investigating the representation possibilities, the best way to represent3D objects like buildings was to use Constructive Solid Geometry (CSG). This type ofrepresentation is capable of describing the vertical walls, which is not possible with a2.5D TIN. After reconstructing the buildings as CSG, these can be added to the TIN.Although this is the best approach for the representation, CSG object could not be addedto the TIN, due to software limitations. Boundary representation was used to solve thisproblem. In CAD, the CSG objects are converted to 3D faces (B-reps). After creatingthe 3D faces, these can be added to the TIN. Due to time limitations, only building E isconverted to the boundary representation using 3D faces.

The 3D historical model for 1885 is shown in Figure 4.8 and the 3D model for 1904 isshown in Figure 4.9.

Apart from the visualisation purpose of the 3D historical model, the model shouldbe able to be used for spatial analysis, i.e. perform calculations, derive information, oranimate past situations. In order to do so, the models have to be georeferenced. Afterthat, complex spatial operations can be applied on them.

Two comparisons were made in an attempt to visualise and quantify the changesthat GeoFort has undergone throughout the years. The first comparison visualises thedifferences between the design of the fortress (1885) with how it was actually built (1904);see Figure 4.10. Next, a comparison is done between the built situation (1904) and thecurrent situation (2007), shown in Figure 4.11. Here, the historical model is combinedwith the 3D model of the current situation (Chapter 5). For both comparisons, the TINis used to subtract the height of each xy-location to a raster. This is done for all TINmodels. Next, the rasters are compared to each other by simply subtracting the cell valueof the second raster from the first raster. This will result in a new raster with in eachcell the height difference as value. This comparison illustrates one application of the 3Dhistorical model.

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Figure 4.8: 3D historical model of 1885.

19

Figure 4.9: 3D historical model of 1904.

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Figure 4.10: Comparison of the models of 1885 and 1904.

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Figure 4.11: Comparison of the models of 1904 and 2007.

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4.5 Quality assessment

To assess the quality of the resulting model, first the sources of errors are investigated.After this the quality is assessed quantitatively by comparing the model with the currentlocation of these buildings and their location in an orthophoto of 1940.

Three types of errors are distinguished in this research: errors associated with thesource map, errors associated with the digitisation process, and errors associated withthe digital representation. Errors associated with the source map include paper stretchesand distortions. In the case of a historical model, these paper stretch problems are gen-erally more intense due to the age of maps. The available maps were provided as digitalphotographs without being properly scanned. This fact introduces geometric distortionerrors, especially in regions away from the center of the map. Moreover, lines indicatingfeature boundaries on the map have a certain thickness. On a 1:1000 scale map, thismeans that a line of 1 mm thickness correspond to 1 m on the ground. All these factorsaffect technical quality of the digitised map. Errors arise also from the digitisation pro-cess. With manual digitisation, the operator is responsible for the overall quality of theprocess. However, even with a highly committed operator, small mismatches between theoriginal and the digital map should be expected. However, these errors can be recognisedand corrected during an editing step, especially for relative small-scale projects such asthis project. Errors can also be associated with the digital representation. Since thedigitisation of a line is a sampling process, the representation of curved lines depends onthe number of vertices used. Following from that, the error of digitising a straight line ismuch less compared to that of a curved line [9].

In the absence of an accurate reference dataset from the era of the map that wasdigitised, an attempt was made to compare the 1904 model with the current situationto check the accuracy of digitisation. For this, Real-Time Kinematic (RTK) GPS mea-surements combined with tacheometric data is used (see Chapter 5 and Chapter 6 for adescription of these techniques). However, there are most probably differences betweenthe state of the fortress in 1904 and the current state since the time span between the1904 map and the current measurements is almost 100 years. This fact that makes thedirect comparison of the two datasets difficult.

As an alternative, an orthophoto of 1940 was used as reference, so that the orthophotocan be compared to the current measurements, and then the 1904 digitisation to the or-thophoto. Distinct features such as bunkers are present in this photo, which was obtainedfrom the Menno van Coehoorn Stichting [36]. The resolution (ground pixel size) of theorthophoto was 0.30 m by 0.28 m. Using an affine transformation model, new coordinateswere computed for the orthophoto relative to the RTK-GPS and tacheometric measure-ments. This georeferencing resulted in ground pixel size of the orthophoto of 0.30 m by0.28 m.

Figure 4.12 displays the overlay of the orthophoto with the measurements taken withtacheometry and RTK-GPS. The green plus signs indicate the sample points taken in theorthophoto, while the red plus signs indicate the corresponding points in the combinedGPS-tacheometric measurements. Table 4.3 shows the point numbers of the samples andthe error residuals of the transformation. The Root-Mean-Square Error (RMSE) of thetwo datasets was 2.27 m. The basis for this transformation were the bunkers, the positionof which is assumed to be stable over the years. Identifying more points in areas othersthan the bunkers, or corner points of buildings, could be unreliable and affect negativelythe results of the transformation. As can be seen the residuals of points on the boundary

23

of the island are considerably high compared to those taken on the bunkers. The reasonfor this could be deformation of the boundary of the island, for instance by erosion or arise of water level. Considering the time gap between the two datasets, one may assumethat the orthophoto fits relatively well to the current measurements. The accuracy ofthe transformation is also influenced by the point-sampling process. The resolution ofthe orthophoto prevents an accurate identification of distinct features, such as corners ofbunkers.

The nature of the transformation also plays a role. An affine transformation considersrotation, scale, translation, and a shear angle. If a conformal transformation was used,which models only rotation, scale, and translation, the errors are expected to be larger.

Figure 4.12: Orthophoto of the GeoFort with superimposed GPS-RTK (green dots) and tacheometry mea-surements (red dots). Georeferencing points are indicated with red crosses (RTK-GPS and tacheometry)and green crosses (corresponding points on the orthophoto).

Table 4.3: Transformation residuals (m) between the orthophoto and the RTK-GPS and tacheometrymeasurements.

Point number 1 2 3 4 5 6 7 8 9 10

Residual (m) 4.019 3.568 3.663 1.383 1.045 0.299 1.709 0.559 1.435 0.848

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The next step is to check the accuracy of the digitised vector map relative to theorthophoto. This is done with the assumption that there were fewer changes of the fortressin the time period between 1940 and 1904 (year of the digitised map) than between 2007and 1940, i.e there are more common points between the digitised map of 1904 and theorthophoto of 1940 than between 1940 and the current status of the fortress. The referencepoints as shown in Figure 4.13 were the basis for the affine transformation of the digitisedmap to the orthophoto. As expected, the residual errors on the boundaries of the islandare larger than for points located more in the centre of the fortress (see Figure 4.13 andTable 4.4). The process resulted in a RMSE of 4.39 m using an affine transformation. Theaccumulated effects of the digitisation process, natural changes of the fortress, and thequality of the paper map explains the magnitude of the error. The same sample pointswere processed using conformal transformation. This transformation resulted in a RMSEof 4.75 m. This difference could be an indication of the magnitude of the distortion of themap that was used during the digitisation process, since the affine transformation modelalso a shear angle between the axis. A conformal model on the other hand, only considersthe shape of an object without taking into account skew components.

Figure 4.13: Orthophoto of the GeoFort, superimposed with digitisation lines (in blue), indicating thedifferences between the digitised map and the orthophoto (red lines).

Table 4.4: Transformation residuals (m) between the orthophoto and the map digitisation.

Point number 1 2 3 4 5 6 7

Residual (m) 3.08 3.14 4.15 4.86 1.09 6.73 5.35

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4.6 Conclusions

The purpose of this part of the project was to create a model that could serve bothvisualisation needs, and as a basis for further spatial analysis. Considering the latter, lostand gained soil volumes between the historical and the present state have been computed.This was an example of spatial operations that can be applied on the model. A TIN datastructure was used for this purpose. Objects that contain multiple heights for each xy-pair were modelled using 3D faces (B-reps). Attributes can be attached to these facesproviding supplementary information to the visitors of GeoFort (such as the purpose thateach building served). Due to time constraints, this has not been fully implemented.

The historical maps were made available by the GeoFort, who digitised the maps usinga digital photocamera. These maps are available at the Menno van Coehoorn Stichting.To create a 3D model from these images, they were digitised using CAD software. Thishas been done manually because of the condition of the maps. Automatic digitisationapproaches have problems working with maps that contain defects such as paper scratchesand ink discontinuities. The creation of the 3D model was done with ArcGIS, which iscapable of both visualising and analysing 3D models.

The accuracy of the final product is strongly related to the accuracy of the dataprovided. In the absence of a reference dataset, an attempt has been made to quantifythe quality of the model. The followed strategy included the comparison of the currentmodel with the orthophoto (1940) and of the historical map with the orthophoto. Theresulting errors were in the order of metres. The reason for the magnitude of the error isthe accumulated effect of digitisation errors, natural changes of the fortress (e.g. erosionof the boundary of the island), and a non-uniform distribution of sample points. Applyingdifferent transformation models, it was found that the geometric distortion of the digitisedmaps was not as high as expected prior to the reconstruction process.

Further improvements of the model could include full implementation of the additionof supplementary information attributes to the buildings, in the form of a database. Inthis way the visitors could click on a building on their handheld and be provided withlocation-based information such as the intended purpose of the building.

Full storage of the different states of the fortress (1885, 1904, 1940, and 2007) in onedatabase in a structured way, will facilitate the combination of the different models of thefortress. In that way, changes over time can be easily visualised and further analysed.

Adding realistic textures to objects will enhance the GeoFort experience of the visitor.Since the currently used software are incapable of reliably exchanging textures, othersoftware approaches should be investigated.

The use of more information sources (e.g. point numbers of the parcels with coordi-nates) for a more accurate representation could be another way to further improve themodel. This kind of information was not initially provided, but it is suspected that theKadaster holds such records. In that way, the georeferencing step could be omitted, thusavoiding transformation errors.

Finally, proper scanning of the analogue maps could also be an option. Even thoughthe distortion of the maps was not as high as initially suspected, proper scanning of themaps would certainly increase the quality of digitisation and the accuracy of the derivedvector map.

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Chapter 5

3D Model - Current Situation

An important part of the geoproducts which are developed during this project is therepresentation of the exterior of the Geofort. This representation will be given in theform of an accurate 3D model of the current situation of the GeoFort. This dataset canserve as a basis for other geo-information datasets. The model will be a combination of amodel of the topography and of the buildings. The model of the topography is normallyreferred to as a Digital Terrain Model (DTM), although no universal definition of a DTMexists. Throughout this chapter, the following definition of a DTM will be used: “A DigitalTerrain Model (DTM) is simply a statistical representation of the continuous surface of theground by a large number of selected points with known xyz-coordinates in an arbitrarycoordinate field” [38]. This representation is statistical, as the xyz-coordinates at andbetween measured points have a limited accuracy compared to the real situation.

The main application of the model will be the visualisation of the Geofort to thepublic. Animations based on this model can be shown both on the Internet as well asduring a visit to the GeoFort. Apart from this purpose, the model is also useful as a toolfor planning and decision-making. If, for example, a new construction is planned for theGeoFort, it can first be implemented in the model to get an overview of the (visual) impactof the planned construction, before actually building it. Furthermore, the model will beused to make a comparison between the historical situation (Chapter 4) and the currentsituation. In line with this is the cultural value of the model: a model of the presentsituation documents the current status of the fortress. This is of particular interest, sincethe GeoFort is part of the Dutch cultural heritage.

This chapter describes the development of a 3D model of the GeoFort, i.e. a DTMincluding buildings. The first section will discuss the requirements of the model. Secondly,a description of the available techniques will be presented, followed by a trade-off studyof these techniques. Third, in Section 5.3, the methodology will be discussed. In thefourth section a description and visualisation of the resulting model is given. The fifthsection discusses the accuracy of this model. Finally, concluding remarks will be presentedregarding the development of the 3D model.

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5.1 Model requirements

The final model requires the (separate) modelling of the terrain (DTM), the buildings,and other supplementary objects. These three components of the model of the GeoFortcan be divided in some sub-requirements, which determine the methods that will be usedfor reconstruction (Figure 5.1).

Figure 5.1: Requirements discovery tree of the 3D model of the GeoFort.

5.1.1 DTM

The DTM should cover the whole GeoFort parcel (the island and outside area), beingapproximately 9 hectares. The representation of the surface should consist of only terrainpoints. As a consequence, non-terrain points like trees, buildings, or other constructionsshould be filtered out. These features will be modelled separately, and later on superim-posed on the DTM. The current DTM will be the basis for comparison with the historicalmodel of the GeoFort (Chapter 4) to reveal possible changes of the topography of theterrain.

5.1.2 3D representation of buildings

The second model requirement is specified by the 3D representation of the buildings.Each building should be modelled separately and its exact location (xyz-coordinates)should be determined. Textures should be applied to these building models to make thevisualisations more realistic.

5.1.3 Other objects

Additional supplementary features like the bridge, trees, and lamp posts will enhancethe level of detail of the model. Texture mapping on these objects further increases thelevel of realism. As the focus of the model is on the terrain and buildings, only a limitedamount of other objects are modelled. In the future, more objects in this category canbe added.

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5.2 Technique determination

5.2.1 Acquisition techniques for the DTM

Different techniques are available to acquire coordinates of terrain points. In this sec-tion, several possible acquisition techniques are compared and tested on multiple criteria.From this, a winning technique is chosen wich will be applied at the Geofort. The follow-ing acquisition techniques are compared for terrain measurements: RTK-GPS, airbornelaserscanning, aerial photogrammetry, and digitisation of cartographic data sources (to-pographic maps).

RTK-GPS

Real-Time Kinematic (RTK) GPS surveys are performed with a data transfer link be-tween reference GPS unit which serves as a base station and a single or multiple roverunits. The field survey is conducted like a kinematic survey, except that data from thebase station is transmitted to the rover units in real-time. One of the biggest advantagesof RTK-GPS compared to kinematic GPS is that no post-processing of the data is re-quired [47]. However, this system does not perform the same under all conditions andin all environments. In forested areas, for example, trees and dense vegetation affect thesignal reception. Therefore, RTK-GPS can only be used to collect survey data in theareas of the GeoFort which are not covered by trees. RTK-GPS has the advantage thatthe surveying data can be collected efficiently: one has to hold position only for a few sec-onds at each measurement location. It can deliver xyz-coordinates with centimetre levelaccuracy almost instantaneously. The maximum range between the base station (fixedGPS receiver) and the rover should not exceed 9-10 km. [47]. This has no implicationsfor the GeoFort, where the largest range is approximately 500 m.

Airborne laserscanning

At present, aerial laserscanning (LIDAR) is one of the most useful and efficient techniquesfor DTM generation, because it allows direct acquisition of 3D coordinates of the terrainsurface. LIDAR is an acronym for LIght Detection And Ranging, sometimes also referredto as laser altimetry or airborne laser terrain mapping. LIDAR systems are classified asactive digital sensors; they emit electromagnetic signals and record the returned signal.The recorded signal is immediately converted to a digital representation and subsequentlystored on a computer [16]. When high accuracy is needed for topographical measurements,airborne laserscanning is one of the foremost data acquisition techniques. In principle, anairborne laserscanning system consists of a laser instrument, a GPS receiver and InertialNavigation System (INS). The GPS receiver provides the position of the airplane, andthe INS provides the attitude of the airplane. The main application areas of airbornelaserscanning are:

� Mapping of surfaces with very little or no texture or poor definition;

� Mapping of forests and vegetated areas;

� Mapping of long narrow features, such as roads, dikes, and rivers [7].

In current ranging laser systems, mostly pulsed lasers are used [55]. Parts of the sentpulse are reflected by leaves or branches of trees, but the last reflected part of the emitted

29

pulse is stored and used to generate the terrain surface. The quality of the results dependson the terrain roughness. Furthermore, some parts of the GeoFort are covered by trees,especially the western part of the area. The major advantages of LIDAR are the highpoint density and the accuracy of the measurements. Therefore, airborne laserscanningwith pulsed ranging is an appropriate data acquisition technique for the GeoFort.

Aerial photogrammetry

Photogrammetry is the science and technology of obtaining spatial measurements andother geometrically reliable derived products from photographs. The fundamental prin-ciple of photogrammetry is to make use of a pair of stereo images to reconstruct theoriginal shape of 3D objects, that is, to form the stereo model, and then to measure the3D coordinates of the objects [34].

Raw aerial photography and satellite imagery have a large geometric distortion thatis caused by various systematic and non-systematic factors. Photogrammetric modellingbased on collinearity equations eliminates these factors efficiently, and creates the mostreliable orthorectified images from raw imagery. DTMs can be generated by photogram-metry from a wide range of photographs and images [33]. Photogrammetric products(such as a DTM and a vector map) are generated in a digital workflow of triangulation,block adjustment, image matching and 3D stereo models [26].

Digitisation of cartographic data sources

Topographic maps can be used as a data source for digital terrain modelling. However, thequality of topographic maps is not always ensured; the quality of the terrain representationderived from a contour map is largely determined by the density of contour lines of thetopographic map and the accuracy of these contour lines. Techniques to digitise analoguetopographic maps are discussed in Section 4.3.1.

5.2.2 Acquisition techniques for modelling buildings

The following techniques are potential methods for modelling buildings and other objects.These techniques are also applicable for indoor modelling, they are described in Chapter 6:

� Close-range photogrammetry (Section 6.2.1);

� Tacheometry (Section 6.2.2);

� Terrestrial laserscanning (Section 6.2.3).

5.2.3 Technique trade-off

It can be concluded from Table 5.1 that three techniques are suitable for constructinga DTM of the GeoFort: aerial photogrammetry, RTK-GPS, and LIDAR. Aerial pho-togrammetry is selected as the best technique, however due to practical problems it wasnot possible to use this technique within the time frame of this project. As an alternative,Both RTK-GPS and LIDAR are used to create the DTM of the GeoFort. For modellingthe buildings, tacheometric measurements were used.

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Table 5.1: Trade-off study for the 3D model of the current situation of the GeoFort. A ‘+’ means that thecriteria is an advantage of the technique, a ‘+/-’ means that it is neither an advantage nor a disadvantageand a ‘-’ means that the criteria is a disadvantage of the technique.

Technique Ver

tica

lac

cura

cy

Hor

izon

talac

cura

cy

Mea

sure

men

tti

me

Pro

cess

ing

tim

e

Dat

abas

esi

ze

Cos

ts

App

licat

ion

dom

ain

RTK-GPS + + +/- + + - Small areas

LIDAR + + + + + - Medium to large areas

Aerial photogrammetry + + + + +/- + Medium to large areas

Map digitisation - - - +/- + Any area size

Map scanning - + - +/- - Any area size

Tacheometry + + - + + - Small areas

Terrestrial laserscanning + + +/- - - - Small to medium areas

5.3 Methodology

In order to create the current DTM of the GeoFort, two basic datasets are used, originatingfrom airborne laserscanning (LIDAR) and RTK-GPS measurements.

5.3.1 AHN data specifications

Airborne laserscanning (LIDAR) data is available as part of the AHN (Actueel Hoogtebe-stand Nederland), which covers the topography of the entire country, including the GeoFort.Rijkswaterstaat is owner of this dataset. The basic AHN file consists of a selection of theoriginal height points as measured using laser altimetry. The selection includes the pointsmeasured on the surface level, which means that errors and measurements on objects andvegetation are more or less removed. Data from rural areas like the GeoFort have beenfiltered during preprocessing, so the data represents only the topography. The ‘Adviesdi-enst Geo-Informatie en ICT’ checked the data on precision and completeness. The pointdensity in the basic file of the AHN is at least one point per 16 m2 [52]. Exceptions tothis are forested areas, with a minimum of one point per 36 m2. For the GeoFort area,the point density is 3 to 5 points per 16 m2 [4].

The accuracy of AHN data is described by precision and reliability. Precision cor-responds to the geometric quality of the data in the basic file, and is described by thesystematic and the stochastic error. The stochastic error, also called point noise, is theerror in the measurements and can be estimated using the standard deviation. For thelaser altimetry the standard deviation is given as 15 cm [4]. The systematic errors areerrors in the coordinate determination with GPS and INS.

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5.3.2 RTK-GPS measurements

To achieve a higher accuracy - centimetre level - new measurements were acquired withRTK-GPS using a LEICA SR530 Geodetic RTK Receiver. The height of the RTK-GPSbase station was acquired by levelling from two known AHN height points at severalkilometers distance from the Geofort. The standard deviation of the new data is given as3-4 cm vertically and 1-2 cm horizontally by the measuring company [8]. The point densityof RTK-GPS measurements is approximately 1 point per 4 m2. The main limitation of theRTK-GPS measurement campaign were the trees in some parts of the GeoFort, blockingthe receiver’s signal and deteriorating the required accuracy. To overcome this limitationto measure the entire GeoFort, it was decided to combine RTK-GPS with the AHN datefor generation of the DTM. More specifically, the gaps in the RTK-GPS measurementswere filled with the AHN laserscanning data (Figure 5.2).

Figure 5.2: Combination of AHN and RTK-GPS measurements.

5.3.3 Software used

ArcGIS [17] was used for:

� Combining AHN and RTK-GPS data;

� Georeferencing of aerial photographs to superimpose these on the DTM;

� Spatial analysis of the topography to estimate the point density of the combineddatasets.

For the modelling of the buildings, tacheometric measurements were performed. Thesemeasurements were processed in AutoCAD [6] and textures were added using SketchUp[23], resulting in a more realistic visualisation (Figure 5.3). Bunkers were measured byRTK-GPS and also processed in AutoCAD.

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Figure 5.3: Visualisation of building models with texture mapping applied.

5.4 Resulting model

After integration of the AHN and RTK-GPS data, a triangulation (TIN) of the measure-ment points was created. In addition, smoothing is applied to reduce the sharp and steeptriangles of the TIN. The resulting DTM is presented in Figure 5.4. When combiningthe DTM with the models of the buildings and other features of the scene, the 3D modelof GeoFort is created, as presented in Figure 5.6. The green shapes on the water (bluepart) are modelling errors (except for the bridge), due to the presence of vegetation nearthe boundary of the water. This vegetation consists mainly of dense shrubs that partlyextent over the water surface. As a consequence, during the triangulation process (TINgeneration), some points that correspond to vegetation are modelled as the water surface.To reduce the effect of these shrubs on the DTM, advanced filtering should be applied,i.e. removing the shrub points above the water.

Additionally, an aerial photograph of 2003 was superimposed on the created DTM(Figure 5.7). This process yielded a more realistic view of the topography. The resultcontains some errors, in particular in the forested areas, because the terrain has changedsince 2003.

Figure 5.4: Top-view of the current DTM of the GeoFort. The presence of vegetation near the boundaryof the water caused some height differences in the LIDAR data. Therefore, the water is not modelledcompletely flat.

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Figure 5.5: DTM of the current status of the GeoFort.

Figure 5.6: Final 3D model of the GeoFort.

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Figure 5.7: Aerial photograph superimposed on the DTM as a texture.

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5.5 Quality assessment

To assess the quality of the DTM, a statistical analysis between the two datasets wasperformed. The aim of this analysis was to check the consistency and consequently anysystematic shifts between the two datasets. An estimation of the error between the twodifferent data sources gives an estimation of the quality of the final product.

A total of 25 points were selected (yellow points in Figure 5.8) in such a way, that theyrepresented the topography variations of the fortress (i.e. from both flat areas and slopes).These points were selected from identical locations on each dataset. For the collectionof AHN points, the TIN was used. The z-value from the TIN was the result of theinterpolation. Therefore, flat areas were preferred over sloping areas, in order to reducethe effect of the interpolation on the accuracy of the heights. The quality assessment wasalso performed with 50 points, but this gave almost the same results as with 25 points.Therefore, it was concluded that 25 points were sufficient to assess the quality of theDTM.

Figure 5.8: Distribution of selected check points.

In order to investigate the consistency of the two datasets and check for systematicshifts, the mean value of the differences of the selected points was calculated, accordingto:

mean =n∑1

zGPS − zAHN

n= 2.6 cm.

According to the AHN product specifications [52], the laser data has a bias of ± 5 cm,which confirmes the observed systematic shift of 0.026 m. The RMSE between the differentdatasets was:

RMSE =

√1

n

∑(zGPS − zAHN)2 = 18.6 cm,

where n = 25 points.

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Figure 5.9: Histogram of height differences between the RTK-GPS measurements and the AHN data.

From the given error of the individual datasets, the error of their difference is estimatedin the order of 15 cm. If δ = zAHN − zGPS, then:

σ2δ = σ2

zAHN+ σ2

zGPS⇒ σδ =

√σ2

zAHN+ σ2

zGPS=√

152 + 32 = 15.3 cm.

This error is close to the one calculated from the selected points, thus no significantdifferences are observed. Flat regions tend to give smaller differences between the differentheight values than sloping areas. Possible explanations for the systematic shift betweenthe two datasets are the different instruments which were used to acquire the data, andthe effect of the interpolation when acquiring the height values from the TIN.

5.6 Conclusions

This chapter described the construction and results of a 3D model of the current situationof the GeoFort. A DTM was created by combining RTK-GPS and AHN data. Althoughthere are some differences between these two datasets, a relative accurate DTM has beenderived when taking into account the apparent similarities between the specificationsand the observed measurement bias and RMSE. The buildings have been modelled withtacheometry and RTK-GPS. Texture mapping has been applied to give the buildings amore realistic appearance.

The combination of the DTM and the model of buildings and other objects providesa basis for many applications, such as visualisations, making comparisons between thehistorical status and the current status of the fortress, and using it for planning andconstruction purposes.

The tacheometric measurements should be completed for some of the buildings, sincenot all buildings are measured yet, due to the limited time available. Furthermore, aerialphotogrammetry would be an interesting technique to apply on the GeoFort, since accu-rate DTMs with realistic textures can be derived from a block of aerial photographs.

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Chapter 6

Interior Model of the GeoFort

GeoFort’s ambition to become the best measured part of the Netherlands does not stopat the exterior of the fortress. An important part of the activities of the GeoFort will takeplace inside the buildings. To support these geo-information based activities, and to serveas a basis for the visitor tracking and tracing system, a detailed model of the interior isrequired.

The focus of this research is on the determination of the measurement techniques tobe used to create the most suitable model. These techniques are then applied in reality;they are used to create a model and the result is checked against the a priori assumptions.

Within this research the model is constructed for one building only, the so-called A-building (see Chapter 2). The other buildings are not yet modelled but this can easily beperformed using the same techniques as applied for the A-building.

In the first section the requirements for the interior model are defined. In the secondsection multiple techniques are discussed and compared to determine the best techniquefor this modelling task. The third section discusses the methodology of the processing tocome to the final model. This final model is presented in Section 6.4 and the resultingquality is assessed in Section 6.5.

6.1 Model requirements

To make a choice between different measurement techniques, the requirements have to bedefined on which these techniques will be compared. These requirements are investigatedaccording to three different purposes of this model and according to the constraints thatrestrict the possibilities for measurements. The three subjects are:

� Visualisation to the public;

� Basis for the tracking and tracing system;

� Construction and interior design of the GeoFort.

They are treated separately in Section 6.1.1, Section 6.1.2, and Section 6.1.3. Section 6.1.4covers the constraints. An overview is given in the requirement discovery tree (Figure 6.1).

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Figure 6.1: Requirements discovery tree of the interior model of the GeoFort.

6.1.1 Visualisation purposes

The first purpose of the model is the visualisation of the GeoFort to the public. Visu-alisations are required for the public to experience the power of geo-information; before,during, and after a visit. At the website a model can be used to show the attractions of theGeoFort. At the fortress itself different geo-information based attractions will make useof visualisations of the model. After a visit, if a visitor wants to recheck on informationthat was provided to him during his visit, the website representation comes at hand. Thewebsite representation can also be used by the visitor to show some of his experiences toother people; thereby promoting the GeoFort.

For visualisation to the public the model must have a realistic look. To achieve this,the most significant real-world features should be represented at the correct location inthe model. To make these features recognisable, accurate textures are required. Therepresentation should be visually attractive, which can be achieved by using vector repre-sentation instead of using a point cloud of data. To support web-based applications andapplications on handheld computers at the GeoFort, the model has to be small in size andhave low demands on computational power. This implies that a vector model is preferredover a raster model or irregular grid (such as a point cloud).

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6.1.2 Basis for tracking and tracing system

The second purpose of the model is that it has to serve as a basis for the tracking andtracing system. It is not sufficient to know the coordinates of a visitor; this is only usefulif it can be related to objects within the GeoFort. Therefore the location of these objectsis needed. The amount of applications and interaction which can be provided to thevisitors by this system depends on the accuracy of which the relation between visitorsand objects is known. The tracking and tracing system delivers the visitors’ coordinateswith a limited accuracy. To make use of this accuracy to the maximum the model requiresa higher accuracy than the coordinates delivered by the tracking and tracing system. Thetracking and tracing system’s coordinate accuracy is not expected to be better than 20 cm.

6.1.3 Construction and design purposes

The third purpose for the model serves the construction and design of the GeoFort itself.Exhibitions, conference rooms, shops and restaurants all have to be designed to fit withinthe GeoFort’s buildings. If an accurate map is available this will simplify the designprocess significantly.

6.1.4 Measurement and time constraints

The constraints that restrict the possibilities for the measurement campaign are relatedto the conditions within the GeoFort and the constraints of the setup of the project.The measurements have to be done in low light conditions, partly in small corridors ofonly 60 cm wide. The complete project has to be finished within 8 weeks, so taking themeasurement and processing the data must fit within this period. Also the used methodmust be available, either at the university or at the cooperating companies.

6.2 Technique determination

The techniques for acquiring the data for the 3D model are compared on the basis of themodel requirements, as outlined in the previous section. In this section first a definitionis given of the different aspects on which the technique determination is based. This isfollowed by a more detailed explanation of the different (technical) aspects and constraintsof each technique separately.

The following aspects are considered in determining the most appropriate measure-ment technique:

Measurement time: the time it takes to measure all the necessary data in order to beable to construct the model.

Processing time: the time it takes to process the acquired data. This includes thegeometric construction of the data and the time it takes to model the objects to avector model.

Point accuracy: the accuracy of the measured points.

Coverage: the number of points in the real world that are covered using the technique.

Texture mapping: the possibility to map textures on the model.

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Application in low light conditions: describes the performance of the technique inareas with low light conditions.

Application in small corridors: describes the performance of the technique in smallareas.

Database size: the disk space needed to store the data and the model on a computer.

To construct a 3D model of the interior of the A-building, three techniques are ofinterest: close-range photogrammetry, terrestrial laserscanning and tacheometry. Thesetechniques are available at the cooperating companies or at Delft University of Technology.In the next three sections these techniques are discussed, in particular their advantagesand disadvantages.

6.2.1 Close-range photogrammetry

In photogrammetry photographs are used to construct 2D (maps) or 3D models. Thistechnique is known from the airborne application, where the camera is mounted on anairplane or a helicopter. In the last decade the application of close-range photogrammetryhas become more popular. Photographs taken from a short distance from the object(metres) can be used to construct a model. The object is photographed from multipleangles, which makes it possible to measure 3D coordinates of the points in a local frame(that of the camera). Using control points, objects can be related to other coordinateframes. By this technique 3D models of buildings can be constructed.

Measurement time

To model an object at least two photographs are required. If a larger object is modelled,more photographs are needed. This number will increase even more when complex objectshave to be modelled, as every point must be present in at least two photographs. Specialattention has to be paid to the points from which photographs are taken (the networkdesign, see for example [5]), especially when modelling large and complex objects. To beable to model all the details, every part of the object has to be photographed.

Processing time

The processing time depends on the number of images. For large complex objects theprocessing can take multiple weeks. When processing the photographs first the interiorand exterior orientations have to be determined. The interior orientation relates the pixelcoordinate system of the photograph to the image coordinate system of the undistortedprojection of the object. Usually this is done during a priori calibration. The exteriororientation gives the position and the orientation of the camera at the time of exposure[19].

After the orientation is known, corresponding points can be identified in adjacent im-ages and coordinates can be measured. From this a model can be constructed. Thismodel is in a local coordinate system. To transform these coordinates to another coordi-nate frame, control points in the photographs can be used. These points can be measuredin advance, for example using tacheometry.

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Point accuracy

The accuracy of the measured points depends on the quality of the orientation. Theunknown location of the camera has to be calculated accurately and calibration of thecamera is essential. If the pixel coordinates are carefully related to known coordinates it ispossible to achieve an accuracy of centimetres. Another error source in photogrammetry isrelated to the control points that are used to georeference the photographs. For example,natural points can be more difficult to locate than clearly identifiable (artificial) points.This will influence the accuracy of the points measured in the photographs. Using blockadjustment, this error will be distributed over all the photographes. This way, errors willnot propagate through the model.

Coverage

All points that are visible in the field of view of the camera can be measured. Thereforethe design of the network is important to cover all objects. The coverage also depends onthe distance from camera to object in combination with the focal length. The closer thecamera is to the object, the lower the coverage.

Texture mapping

A large advantage of close-range photogrammetry for constructing 3D objects is the avail-ability of the textures from the photographs. Once the objects have been modelled thephotographs are already linked to this model.

Application in low light conditions

The big disadvantage of photogrammetry is the need for light, since photographs onlyrecord visible electro-magnetic radiation. Poor light conditions will make it difficult todistinguish between objects in the photographs and will influence the accuracy of theend result. In case of the GeoFort, the light conditions on the scene are poor. The onlyluminosity is from fluorescent lamps mounted on the ceiling. If this technique is appliedmovable artificial light sources will probably be necessary. These light sources are visiblein the photographs and thus in the textures, and therefore have to be used carefully.

Application in small corridors

The coverage of a photograph depends on the distance from the camera to the object.When a small corridor has to be modelled, many photographs and therefore also manycontrol points are needed to be able to combine the photographs. It is possible to usephotogrammetry, but it will result in many photos which increases the processing time.

Database size

From the photographs a vector model is constructed, representing the objects. The origi-nal data (the photographs) require much more storage space. The database size dependson the number and complexity of the objects, and on the resolution of the photographs(and thus the textures).

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6.2.2 Tacheometry

Tacheometry is a widely known technique for land surveying. The tacheometer instrumentis often referred to as a ‘total station’. It was a popular technique for constructing 2Dmaps because of the high accuracy. With the development of GPS, that can achievecentimetre accuracy nowadays, tacheometry has become less popular since it is morelabour intensive than the use of GPS. The main drawback of GPS is that it requires aclear signal from space, which is not available at indoor locations.

In tacheometry distances and angles are measured with a tacheometer, using electro-magnetic signals (Figure 6.2). A signal is sent actively by the machine and reflected ona small spot on a wall or a special reflector. The travel time and angle of measurementare recorded. If the position of the tacheometer is known, the coordinates of the points ofinterest can be calculated using the measured distance and horizontal and vertical angle.The different base stations of the tacheometer are connected through a network, of whicheach next point is carefully measured from the previous point. All points are connected tomultiple reference points. In this way it is possible to achieve high accuracy coordinatesfor the measured points (millimetre accuracy) and to have all coordinates in the sameframe as the reference points.

Developments in tacheometry are the robotic total station and the pen computer. Forthe first system, the total station is able to follow the reflector. The advantage is thatthe measurements can be done by one person, where with older systems require at leasttwo surveyors. The pen computer shows the observations directly on screen. This hasthree advantages: first, when two measurements to the same point differ too much, themeasurement can be repeated and checked again. Second, since it is also possible tocreate the objects on the pen computer, a large part of the processing is done during themeasurement campaign. Third, because the user immediately sees the recorded points, itis easy to check if all needed objects are measured and it will not be necessary to revisitthe location for extra measurements.

Figure 6.2: The distances and angles measured by a tacheometer.

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Measurement time

For tacheometry the measurement time depends on the complexity of the object. Toconstruct a wall only the corners of the walls are measured. For each measurement pointa few minutes are needed to determine which point has to be measured and to do themeasurement. More objects means that more points need to be measured, resulting inlonger measurement time. Part of the measurement campaign is the construction of anaccurate network. The construction of the network points is done by placing markers ornails which have to be measured carefully. After determining the new network point thetacheometer has to be moved to its new location. Also the reference points have to belocated, using for instance GPS.

Processing time

Processing of the tacheometric data starts with the calculation of the position of thetacheometer. With the use of these positions, the coordinates of the measured points canbe calculated. The calculated positions can be checked from multiple reference points andmultiple paths to these reference points, making sure that there are no large errors in thedata. The measured points are used to create lines and planes that represent the objects.In case of the pen computer, a large part of the processing is done during the measurementcampaign, reducing the processing time needed afterwards. After the campaign all pointsand important lines are already modelled, only planes and textures have to be fitted.

Point accuracy

To achieve a high accuracy the network points are very important. When these coordinatesare poorly determined, the model will not be accurate. A good network allows to check themeasurements from multiple positions and if necessary check and improve the consistencyon the spot. Using tacheometry the accuracy can be smaller than one centimetre. Fordistance measurements the error is usually a few millimetres. For angle measurementsthe error is around one milligon. Important to note is that this is only the accuracy of themeasurement. The accuracy of the resulting plane can be lower because of the non-flatstructure of the walls.

Coverage

The coverage of tacheometric data is low, since only the necessary points are measured. Itis not possible to add points afterwards, unless a new measurement campaign is performed.

Texture mapping

Tacheometry does not give any information about the textures of objects. Textures froma different source can be mapped on the model using reference points such as corners.

Application in low light conditions

Tacheometry makes use of electromagnetic signals in the infrared domain. Strictly speak-ing, it is possible to use tacheometry at locations with very poor light conditions. However,

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in order to be able to direct the tacheometer towards the points to be measured, it is con-venient to be able to see the points. The light conditions can influence the accuracy, sinceit will affect the visibility of the points.

Application in small corridors

In theory it is possible to use tacheometry in small corridors, but it is not the mostconvenient method to use. First there has to be enough space to place the tacheometer.This area is approximately a circle with a radius of 0.5 m. Second, a (reflectorless)tacheometer can have minimal measurement distance of about 1 m.

Database size

The tacheometric data requires not much disk space, since only a limited number of pointcoordinates are stored. Even if lines and planes are stored, the model size is relativelysmall.

6.2.3 Laserscanning

Laserscanning is one of the newest techniques for constructing 3D models. There aremany industrial applications, for example the modelling of pipelines. Laserscanning isalso used to create 3D city models.

The laserscanner sends laser signals, deflected by a mirror to a certain angle. Thedistance to an object is measured using time of flight or phase shift measurements. Thetime of flight technique measures the time difference between sending and receiving thesignal. Knowing the speed of light, the distance can be calculated. Phase shift measure-ments are more accurate because of their independence of timing. This technique is basedon measuring the difference between the phase angle at the moment of sending and thephase angle on the moment of receiving. Using multiple (usually two or three) frequen-cies modulated over one signal a large wavelength is used to determine a rough distance.More precise distance measurements are done using the smaller wavelengths. When anobject is too close, the phase shift on the long wavelength is too small and therefore theambiguity of the small wavelength cannot be solved. To be able to take a measurementthe measured phase difference must be large enough, causing the minimal measurementdistance to be limited.

Figure 6.3: Modulation of the laser signal for the phase shift distance measurements (source: [18]).

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Measurement time

The laserscanner can send pulses at a very high rate. The maximum scanrate is up to500,000 points per second, but the scan rate influences the resolution. Depending on thedesired point density, a full scan of the surrounding environment (360 degrees horizontal)will take between ten and thirty minutes, containing more than one million points. Sometime for setting up the scanner must be taken into account.

Part of the measurement campaign is to measure the location of targets in the laserscanwith a high accuracy. These locations are needed to be able to transform the measure-ments from a scan to another coordinate frame accurately.

Processing time

Every separate laserscan is measured in a local coordinate frame. To combine all thescans to one large point cloud, objects appearing in two scans can be used to combinethe scans. To achieve a higher accuracy of the combined point cloud, it is better to useartificial objects or targets, like reflectors or balls. At least three corresponding points orobjects have to be scanned in two separate scans to relate the scans to each other. It isalso possible to use the coordinates of these targets from a different source, for instancefrom tacheometry measurements. In this way there does not have to be overlap in thescans.

The next step is constructing a vector model from the point cloud. Unfortunately,there are still a lot of problems in automatic recognition of objects from point clouds.Algorithms to extract lines and planes exist, but it is difficult to automatically modelmore complex objects. So, a large part of the modelling has to be done manually fromthe point cloud. Depending on the complexity of the objects and the experience of theuser, modelling can take up to weeks.

Point accuracy

There are three different scanners that can be used, namely close-range, mid-range andlong-range scanners. The accuracy depends on the type of scanner that is used. Accordingto the producer [32], the laser scanner can achieve an accuracy of 5 mm over 10 m distance.

Coverage

Modern laserscanners cover 360 degrees in the horizontal direction and 310 degrees in thevertical direction. A point cloud can consist of more than a million points. All objectsin the line-of-sight of the scanner reflect the laser signal. As with photogrammetry thescan only shows visible objects. Mirrors and windows can cause refraction of the signal,making these measurements unusable.

Texture mapping

Except for distances and angles, also intensities of the returning signal are measured.Visualisations of these intensities look like a black and white photograph. This imagecan be used to display the texture of an object, but since it represents the reflection ofthe laser, the result does not look realistic. Some laserscanners can take (low resolution)photographs while scanning, so no extra photographs are needed for texturising.

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Application in low light conditions

Laserscanning is an active system, implying that it does not use (day)light for its mea-surements. Therefore this technique can be used in low light conditions as well.

Applications in small corridors

As with the tacheometer, the laserscanner cannot be used conveniently in small corridors.When using a phase shift scanner, objects closer than one metre cannot be measured. Fortime of flight measurements this is not a problem.

Database size

One laserscan consists of more than one million 3D points. For one such scan, approxi-mately 20 MB of memory is needed. Buildings where multiple scans are needed will needmore memory and, more important, enough computational power to display the pointclouds. Processing these point clouds to vector representations will lower the neededdatabase size significantly.

6.2.4 Technique trade-off

As can be seen in Table 6.1, each technique has specific properties compared to the others.The required model does only have to cover the distinctive features of the fortress. Fur-thermore the project has to be finished within a few weeks, which could result in problemsfor the long processing times of laserscanning and photogrammetry. If photogrammetry isopted for, the low light conditions are a large risk to the success of the project. To ensurethat the model will be finished within the available time tacheometry is the preferredtechnique.

Table 6.1: Trade-off study for the 3D interior model of the GeoFort. A ‘+’ means that the criteria is anadvantage of the technique, a ‘+/-’ means that it is neither an advantage nor a disadvantage and a ‘-’means that the criteria is a disadvantage of the technique.

Technique Mea

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Photogrammetry +/- - +/- + + - - +/-

Tacheometry +/- + + - - + - +

Laserscanning + - + + - + - -

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The downside of a tacheometric model is the low level of detail. This can be counteredby adding information with the use of one of the other techniques. Because of the suit-ability for low light conditions, laserscanning is applied as a complementary technique.The laserscans are referenced to the tacheometric model, allowing transfer of data pointsand lines from one model to the other.

6.3 Methodology

As concluded in the previous section, the selected technique for measuring the interior ofthe A-building of the GeoFort is a combination of tacheometry and laserscanning. First,the instruments used will be described. The measurement setup and conditions whichcome along using these techniques will be described in the following sections. After thisthe applied modelling techniques and software are discussed.

6.3.1 Used instruments

Table 6.2 shows the specifications of the Leica HDS4500, the laserscanner used. Thespecifications of the tacheometer Trimble S6 are given in Table 6.3.

Table 6.2: Specifications of laserscanner Leica HDS4500.

Leica HDS4500

Accuracy single point positioning ≤ 6 mm at 10 m

single point distance ≤ 3 mm at 120 ppm

angle 350 µrad (horizontal and vertical)

Range 1 - 25 m

Scan rate up to 500,000 points / second

Field of view horizontal 360 degrees

vertical 310 degrees

Number of points horizontal 20,000

vertical 20,000

6.3.2 Measurement setup

To reference the resulting model to ‘Rijksdriehoek’ coordinates (RD, Dutch referencecoordinate system), two GPS reference points are used. During the measurements takenfor DTM creation (see Chapter 5) an AHN height was levelled at a fixed nail on the bridgeof the GeoFort. From this nail the location of the two GPS reference points is determinedusing RTK-GPS. This technique is described in more detail in Section 5.2.1.

These points are used as starting points for the tacheometric measurement campaign.From a known point the tacheometer measures angles and distances to each new mea-surement station of the instrument, making this point also known. If possible, nails wereplaced at each new measurement station, resulting in a permanent measurement network.

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Table 6.3: Specifications of tacheometer Trimble S6. In the tracking mode, the total station finds theprism automatically. This is referred to as robotic total station. The limited accuracy is caused by theautomatic aiming at the centre of the prism.

Trimble S6

Accuracy distance prism mode standard 3 mm + 2 ppm

prism mode tracking 10 mm + 2 ppm

reflectorless mode standard 3 mm + 2 ppm

reflectorless tracking 10 mm + 2 ppm

angle 1.0 mgon

Minimum range with prism 0.2 m

reflectorless 2.0 m

If it was impossible to place a nail, for instance on concrete surfaces, the point was markedtemporarily by a marker. In total 10 nails were placed and 4 marker points were used.For the best accuracy possible, the objects have to be visible from the network points,to make sure direct measurements are possible. So, within the A-building, at least onenetwork point per room is needed. From each network point the points of objects in thefield of view are measured. During one single day the pen computer system was availablefor this research. It proved possible to measure almost all corners of rooms, doorways,and the placed targets for the laserscanner. Although measurements were performed onlyon these points (thus leaving out any more detail), room A2 at the end of the corridor(see Figure 6.6) could not be measured except for the placed laser targets, due to the timeconstraint.

The laserscanning campaign used the tacheometric data to reference the scan points tothe same RD coordinates. At each measurement station of the scanner four targets were inthe field of view of the scanner. These points were scanned at a higher resolution to obtaina precise position in local scan coordinates. Later on in the process, the coordinates of thetargets are used to register the scans in Cyclone (see Section 6.5). It proved possible toscan all rooms and the corridor of the A-building in only one day. The a priori estimationof half an hour per scan proved to be fairly accurate. Each scan contains approximatelyone million points plus the points obtained from the higher resolution scan of the targets.These points cover all objects in the line of sight of the scanner.

To cope with objects outside the line of sight of these instruments, points are addedby measuring them by handheld laser distance meter or tape measurements.

6.3.3 Modelling software

To combine the acquired data three different software packages are used. In this section,a description of the software is given, as well as the application of the software in thisproject.

Microstation [37] is a drawing and design program used in the fields of civil engineering,architecture, and surveying. For companies in the geo-information sector, Microstation isan interesting software package because it is not only a CAD drawing program; GIS oper-ations can be applied directly within the software. Microstation models can be exported

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to ArcGIS or other CAD programs. For many surveying companies, Microstation is thestandard drawing software [37]. The used pen computer system is based on Microstation.Because of this integration and the wide use by the industry, Microstation is chosen tocreate the vector model of the interior of building A.

Cyclone [12] is a software package developed by Leica Geosystems to process 3D pointclouds. It can be used as an operating interface for the laserscanner, to register thepoint clouds and to model the point cloud. The choice for Cyclone was easy, since a Leicalaserscanner is used and Cyclone is developed to work with data from a Leica laserscanner.

Google SketchUp [23] is used to create faces and map textures to the vector modelcreated with Microstation. SketchUp has been chosen because it proved to be a convenientprogram to match planes and textures on the model. It is a straight-forward programwith a fast learning curve. The disadvantage of SketchUp is that is has trouble dealingwith non-planar surfaces. Therefore only the lines are imported in SketchUp. Wherenecessary, additional modelling was done in SketchUp, to make sure all faces were mappedwith textures.

6.3.4 Processing steps

Starting point for the processing is the model of the measured tacheometric data. Someprocessing is done during the measurement campaign. The pen computer system deliversa vector model including all tacheometric measurement points, connected by vectors. Themissing objects in this model are added in Microstation with the data from the handheldlaser distance meter and tape measurements.

Laserscans are referenced to RD coordinates by inserting target coordinates in Cyclone.Now all scans can be combined to form one point cloud, covering the entire building. Moreobjects are added to the vector model from this point cloud.

To add these objects, two different approaches are possible. One is to model anobject in Cyclone and export it to Microstation. The other possibility is to measure thedimensions of the object and then draw it in Microstation. For the first option, differentstrategies are available. Using Cyclone, it is possible to fit a variety of geometric objects toa part of the point cloud. Lines, planes and corners are simple shapes, but more complexobjects like cylinders or spheres are also possible. Automatic fitting is still a problemin the point cloud processing. For example, when one wants to fit a plane, the pointsrepresenting the plane have to be selected, since erroneous points influence the result ofthe fit. Still a lot of human intervention is needed in the modelling process. If the correctpoints are selected, the fit is accurate (better than millimetre accuracy). Instead of fittinggeometric objects to the point cloud, only the corners of an object could be fitted. Thisresult can be exported to Microstation.

The second approach is to measure dimensions in the point cloud. By this geometricinformation of an object is obtained very quickly. The dimensions can be used to recon-struct the objects in Microstation. In this project, this approach is used to model themissing objects.

The 3D vector model is imported in SketchUp, where faces and textures are added.Lines are added to create faces on non-planar surfaces by triangulation. Photographs areedited in Adobe Photoshop [2] to create textures of small size covering only the objectsto be mapped. These textures are imported and fitted in SketchUp. It is important tonote that the resolution of the photographs will influence the experience of the user. Thehigher the resolution, the more details are included in the model and the more realistic the

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model will look. The drawback is that high-resolution photographs need more memoryspace. This conflicts with the request for a small and easy-to-load model. The resultingmodel can be easily exported to a website or Google Earth to give a web representationof the GeoFort.

6.4 Resulting model

From the different modeling steps several models are derived. The first model is the pointcloud model consisting of all points from the laserscans. The second model is a vectormodel consisting of the vector boundaries of the building. Also, from this 3D modela 2D map is derived. In the final model surfaces are added to the vector model andphotographic textures are mapped to these surfaces. All three models have a differentpurpose and are discussed separately in the next sections.

6.4.1 Laserscan model

The laserscan model consists of laserscan measurement points referenced to RD coordi-nates by target coordinates, as described in Section 6.3.2. The advantage of representingthe building directly by the obtained point clouds is that no information is omitted. Allpoints measured by the laserscanner are present in the result and no interpolation oraveraging has been applied. This model gives the most detailed and accurate geometricrepresentation of the actual situation. A visualisation of the model is shown in Figure 6.4.

Figure 6.4: Laserscan model of building A.

6.4.2 Vector model

The vector model uses the tacheometric data as a basis, to which points are added that aremeasured by laserscanning, by a handheld laser distance meter, or by tape measurements.The entire A2 room, which is located at the end of the corridor, is added using laserscandata. All points are taken at manually chosen locations physically at the building orfrom the point cloud data. Vectors are based on these points, forming the start and endlocations. This way only a few points are needed to represent this model, thereby reducingthe model size and increasing the rendering speed of visualisations. However, by using

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only a few data points to construct a line, information is lost and in fact vectors could bebased on points which are not representative for all the points on the plane. Therefore,the resulting model gives a good and accurate overview of the building, but leaves outsome of the detail of the laserscans. The vector model provides accurate information thatcan serve as a basis for construction and interior design purposes. A visualisation of themodel is shown in Figure 6.5. From this 3D vector model a 2D map is created to supportthe design of the tracking and tracing system of this project. This map shows the top viewof the floor plan of the 3D model (Figure 6.6). All vectors above the floor are removed.

Figure 6.5: Vector model of the ground floor of building A.

Figure 6.6: 2D map of building A, derived from the 3D vector model.

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6.4.3 Texturised 3D interior model

Faces and textures are mapped to the 3D vector model, which results in a visually at-tractive model with recognisable photographic features. In this model vectors are addedto create faces on non-planar surfaces. These vectors are not measured but interpolatedbetween points in the vector model. This introduces inaccurate information to the vectormodel representation, but the resulting model is easier to interpret visually than a modelconsisting of only vectors. This model is shown in Figure 6.7.

Figure 6.7: 3D interior model with texture mapping applied.

6.4.4 Data size of the models

Table 6.4 gives an overview of the data statistics for the three different models. It is clearthat the vector model is convenient to use on a handheld with limited computing power.This model only contains lines; no planes are defined. This has a negative influence onthe orientation for the user, since it is difficult to see any 3D structures on the 2D screen.The use of planes will improve this.

The point cloud requires a lot more memory. This can be a disadvantage for thehandheld of the visitor, although the memory capacity for handheld devices grows quickly.The screen size may limit the usability of the point cloud as a model. Modelling the pointcloud with geometric objects will greatly reduce the required memory capacity.

The data size of the SketchUp model depends on the textures. Without textures, themodel is ± 1.0 MB. Depending on the resolution of the photographs used for the textures,the size of the model can grow quickly. A model of this size can easily be viewed on aPDA. In this case, textures are in general stored as 72 DPI in JPEG format, resulting inan image size per plane of approximately 20 KB.

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Table 6.4: Data statistics for the three different models. For the SketchUp model the data size includingtextures is given. Without textures this model is 1.0 MB.

Vector model Point cloud SketchUp model

Data size 148 KB 284 MB 2.3 MB

Number of points - 17,374,287 -

Number of lines 780 0 4740

Number of planes 0 0 2254

6.5 Quality assessment

For the final model two different acquisition techniques are used, tacheometry and laser-scanning. Both techniques have a different accuracy, where tacheometry is assumed tobe more accurate. In this section the accuracy of both methods is treated, as well as theestimated accuracy of the model.

6.5.1 Tacheometry

The accuracy of tacheometry was estimated beforehand as better than one centimetre. Toachieve this accuracy, it is important to have an accurate network of points throughout thebuilding. At least two known points are used to reference the network of points. These twopoints have an accuracy better than one centimetre. The error in these positions is presentin all network point coordinates. The network points are used to perform more detailedmeasurements in the rooms. Combined with the few millimetres standard deviation ofthe measurements the total error is expected to be in the order of one centimetre.

The process described above is the ideal situation, which will result in the best possibleaccuracy for the model. Due to time limits (there was only one day available to do themeasurements) a slightly different approach is used. The network points are measured on-the-fly. To calculate the position of a network point, other network points and laserscantargets are used. This gives a control on the accuracy of the new network point. When theerror is too large, the measurements are repeated. If the accuracy is sufficient, detailedmeasurements are performed. Due to the environment in the remises, it is difficult tomeasure corners of a wall accurately. Reason for this is that the corners are not clearlyidentifiable because of debris, which makes it impossible to measure the exact corner node.For all the network points, the accuracy is a few millimetres. The measurements start atone known point outside the building and end at another known point. At the end, theclosing error may not exceed a certain boundary, which is usually a few centimetres. Thelast step is to adjust the complete network to the two known points. The accuracy of theresulting model is estimated as a few centimetres.

A problem in this measurement campaign was the staircase in remise A1. To guaranteethe accuracy of a network point, at least two points are needed that are measured from aprevious network point as well as from the new network point, but three points is preferred,since this increases the redundancy. The first network point on the ground level in remiseA1 was obtained using only two control points, one temporary point halfway the stairsand a nail next to a small brick wall. The steep stairs caused an error in the networkpoints on the ground level. Usually a closing error of a few centimetres is accepted, in

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this project it turned out to be one decimetre. Because the network point on the firstfloor (which is the entrance in remise A1) is close to the first reference point and all thenetwork points are measured in the same way, this error turned out to cause a rotationabout this network point of the ground floor relative to the first floor. After a correctionfor the assumed rotation, the closing error was only a few millimetres.

The accuracy is not known exactly. Preferably, all measurements are stored andadjusted afterwards. In this adjustment, the observations and their covariance are usedto estimate the unknown parameters. Also the covariance of the coordinates is calculated,which gives the accuracy of the model. Even when using pen computer tacheometry, it ispossible to store the measurements and adjust afterwards, or to adjust on-the-fly. Noneof these techniques were applied in the project, because the company involved in themeasurement campaign does not store these measurements. In order to quantify theaccuracy of the model, it is recommended to store the observations.

6.5.2 Laserscanner

The accuracy of the distance measurements using the laserscanner as provided by themanufacturer is 5 mm per 10 m. Important to note is that this is the accuracy under idealconditions. Factors that influence this accuracy are for instance the reflecting propertiesof the material of the objects, light and temperature conditions, and the colour of theobject. The resulting accuracy of the points in one scan image will be better than onecentimetre, which is more accurate than expected during the technique trade-off.

The laserscans are registered using the position of the targets. Registration is theprocess of identifying identical objects in multiple scans, to be able to combine the scans.The position of the targets is determined using tacheometry, with sub-centimetre accuracy.In the registration process, the targets are set as constraints. For every target, the errorvector to its new position is calculated. If the error is too large - in this registrationmore than one centimetre - the constraint is not used in the registration. The meanabsolute error of the 60 used constraints is 3 mm. The mean absolute error of the 3unused constraints is 17 mm. Together with the specified accuracy of the instrument, theoverall accuracy of the laserscan data will be around one centimetre.

With the use of tacheometry for the targets, the coordinates of the targets are known.This will not always be the case. To achieve an accurate registration of the laserscans,two separate scans should contain at least three common objects, natural or artificial.The latter are usually the best, because they are clearly distinguishable.

A problem with laserscanning is that the origin of the reflection is not known. Thiscauses problems when edges of objects are determined, since the reflection is not necessar-ily coming from the exact edge of the object. The resolution of the footprint is 6 mm on adistance of 10 m, so on larger distances the error will be larger. For manual measurementsin the point cloud, visual checks are done to make sure the best representing points areused.

6.5.3 Modelling

Most of the modelling is done using Microstation. Objects not measured using tacheo-metry are measured in the point cloud and drawn in Microstation. As stated before, theexact reflection point of the laser signal is unknown. Selecting a point that representsthe corner of an object cannot be done without errors. Depending on the distance of the

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laserscanner, the distance between two points in the cloud can be up to several centime-tres. Using a wrong point when measuring the dimensions can result in an error of a fewcentimetres.

SketchUp is used to add textures to the model. Exporting the model to SketchUpcaused non-planar surfaces created in Microstation to be deleted. Where needed, modi-fications are applied to solve this. The important lines (edges of walls, floors) are intactand surfaces are triangulated such that SketchUp can create faces on these surfaces.

The curved ceilings throughout the building proved to be a challenge to model. Cy-clone does not support planes with curved edges (or patches as they are named in Cyclone).SketchUp divides a curved plane in several planes that represent the curved surface. Thistechnique could be imitated manually to model these surfaces in Cyclone. Microstationallows complex shapes, but the insertion of these curved surfaces does not always workflawless. Separating the ceiling into several smaller planes is a solution to the problem. Tocreate surfaces in Microstation proved to be much more time consuming than in SketchUp.

6.5.4 Accuracy of end result

When two different techniques are used and combined it is important to look at the accu-racy of the end result. Tacheometric measurements itself are very accurate, in the orderof millimetres. The same holds for the laserscanning measurements, with an accuracy ofless than one centimetre. The problem for tacheometric measurements is that errors occurwhen corners of the rooms are measured, because they can be difficult to identify as aresult of the dust and other dirt that is present in the corners. This can result in an errorof a centimetre. Points in the laserscan point cloud can be hard to identify, because theorigin of the reflection is unknown. The distance between two points in the point cloudcan be a few centimetres. With all this in mind, the accuracy of the model is estimatedas a few centimetres with respect to the real RD coordinates.

6.6 Conclusions

The request for attractive applications on websites and handheld computers showed theneed for a small vector model consisting of the most prominent indoor features. Photo-graphic textures are mapped to this model to give it a realistic look.

From the defined requirements tacheometry was chosen as the most suitable mea-surement technique to create this model. In combination with pen computer technologythis technique allows a very short processing time to create an accurate vector modelwith a low amount of detail. To add more detail to this model, point coordinates fromlaserscanning point clouds were added.

It proved possible to measure almost all corner points of rooms and doorways by ta-cheometry in one day. All rooms could be covered by 12 laserscans in one day. Tacheomet-ric data was processed using the Microstation software package, points from the laserscanswere added using the Cyclone software package. Surfaces were modelled using SketchUp.This software was also used to add photographic textures.

Four models are created. A point cloud model gives the most detailed and accurategeometric representation of the actual situation. A 3D vector model provides accuratedimensions of the building which can serve for construction and interior design purposes.A 2D map is created and used as a basis for the tracking and tracing system. The accuracy

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of this map is better than the required 20 cm. A 3D model with faces and mapped texturesis created to use for visualisations to the public.

It is not advisable to focus on improving the accuracy of the created model as itis already very close to the natural roughness of the building. The amount of detailof the models could be improved by adding more detail from the laserscanning pointclouds to the vector model. Since this data is already available, this only requires manualprocessing. Further research could be performed on the integration of the 3D model intoa three-dimensional tracking and tracing system. The available visualisations are at themoment still static. Possibilities could be investigated to use this in an interactive way,for example by providing object information.

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Chapter 7

Design of a Real-Time VisitorTracking and Tracing System

The main function of the GeoFort will be to let visitors explore the world of geo-informationin an educative way. The GeoFort will provide visitors with an interactive tour through-out the fortress, rather than having the more static approach of traditional exhibitions.To offer a more dynamical approach and to let visitors make contact with navigation as animportant part in the field of geo-information, they will navigate throughout the fortressenjoying location-based information on-the-spot. For this, a real-time visitor trackingand tracing system (also known as a Real-Time Location System, RTLS) needs to bedeveloped, which is the topic of this chapter. First, the requirements of the system arediscussed, followed by the trade-off of positioning techniques. After that, the results of amarket investigation on positioning systems will be presented.

Two pilot positioning systems have been tested in the A-building of the GeoFort: onewith WiFi as positioning system and one with ultrasound. The focus is on the indoorperformance of these systems. The description and results of the pilots are describedin Section 7.4 and Section 7.5, respectively. This chapter concludes with a comparisonbetween these two pilot systems, and a future outlook on the further development andimplementation of the real-time visitor tracking and tracing system.

7.1 Requirements

The requirements for the real-time visitor tracking and tracing system are:

� Suitable up to 500 visitors;

� All visitors must be uniquely identifiable;

� Places where the visitors are in space, the routes they have taken, and the time theyhave spent on their tour must be stored;

� Location and route information of visitors must be directly available to a centralcomputer;

� Location and route information of visitors must be directly available at the infor-mation device the visitors carry with them;

� Location and route information of visitors must be directly available on the internet;

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� The information device must be solid and protected against theft;

� The information device must be able to show readable text, movies and sounds withearphones;

� Operating costs must be as low as possible.

These goal requirements set some demands on the positioning system, the visitor’shandheld, the data network infrastructure and the user interface. These will be discussedin the following sections. An overview of the requirements is given in Figure 7.1.

Figure 7.1: Requirements discovery tree of the visitor tracking and tracing system.

7.1.1 Positioning system

The positioning system must be able to deliver 3D position coordinates (x,y,z) to thevisitor. Each visitor carries a handheld with a unique ID, in such a way that up to500 visitors can walk around the GeoFort and still receive their own position. This impliesthat the ID of a handheld needs to be broadcasted or transferred to the positioning system.

Both the outdoor and indoor environment of the GeoFort will be covered. This im-plies that either a technique is needed that can be used both indoor and outdoor, or anintegration of different techniques is needed.

Since the position information will be used for Location-Based Services (LBS), theaccuracy of the system must be good enough to be able to provide LBS. In practice this

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means that the positioning system must be able to give positions with an accuracy betterthan the ‘object-spacing’ inside the GeoFort. Here, object-spacing refers to the distancebetween the places where LBS are provided. For example, consider two objects A and Binside a room of the GeoFort that both have information stored in a database that will bepresented to the visitor when he or she is standing in front of the object. The accuracymust be good enough so that the positioning system recognises which object the visitoris facing, either A or B, in order to provide the right information. If the accuracy islarger than the object-spacing, additional techniques need to be implemented to be ableto distinguish between objects.

The vertical accuracy is of less importance. It is only necessary to know the levelwhere the visitors are, rather than having full xyz-coordinates.

The visitors must experience the navigation as real-time. Thus, the latency of thepositioning system must be within such bounds that the user still thinks it is real-time.

7.1.2 Handheld

The basic requirement of the handheld is that it can communicate with the positioningsystem, i.e. that it can receive the position information and that it can broadcast its ID.

The handheld must be able to show textual information, images, and movies. This willbe done by a combination of audio and video, i.e. earphones and a display are required.

The handhelds should be able to withstand a fall from at least 1.5 m, when a visitordrops it by accident. This solidness can be achieved by having some cover around thehandheld, for instance one made of rubber. The display of the handheld should be thickenough to withstand a fall.

The handheld must be protected against theft. This is relatively easy to implementat the GeoFort, since it is only accessible by one entrance (the bridge). A sensor systemthat detects outgoing handhelds and subsequently triggers an alarm is sufficient.

Finally, it is important to have handhelds with a long battery life, so that it is opera-tional for at least the duration of the stay of the visitor.

To keep the operational costs as low as possible, off-the-shelf available handhelds arepreferred. Then the handhelds are easily replaceable when needed.

7.1.3 Data transfer

A wireless data transfer network is required to send the location information of eachvisitor to a central computer. This information needs to be sent in real-time, implyingthat a network with full coverage is needed, both outdoor and indoor.

7.1.4 Interface

A more conceptual part of the development of the tracking and tracing system is theoutline of the interface. The identification of the visitor is based on a unique ID, attachedto the handheld device, together with the profile of the visitor. This profile is definedbased on some input data the visitor provides prior to the start of his tour, consisting ofthe visitor’s age, sex, background (education, job), interests (e.g. history, science, art),and the time he/she wants to spend in the GeoFort. An algorithm needs to be designedto categorise a visitor into a certain pre-defined profile, based on these input data. Thenumber of different profiles can be associated with a number of different ‘information tours’

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available in a database. The tour can then be adjusted to the interests and background ofa visitor. An important requirement is that the input of the data is an easy and quick task,which can be performed by each visitor without the need of instructions (self-explanatoryinput interface).

Software needs to be developed to store the position and time information of thevisitor. Algorithms are needed to calculate the route the visitor follows, as well as thetime a visitor spends at different locations in the GeoFort. This information can then beused to adjust the tour information, since this is related to the position and route of thevisitor.

The information about visitors (route, time, profile) must be directly available to thecentral computers of the GeoFort. In this way, GeoFort is able to gather statistics aboutthe routes the visitors follow and the time they spend on each location, combined withthe corresponding visitor profile. Furthermore, they can keep an eye on the visitors, forexample to see if they do not enter areas where they are not supposed to go to.

Finally, an algorithm should be developed that publishes the visitor information au-tomatically on a website on the Internet.

7.2 Determination of positioning techniques

This section describes six positioning techniques together with an assessment accordingto eight analysis aspects, mostly applied to the GeoFort as the deployment site. Fromthis evaluation the advantages and disadvantages come forward on which the trade-offis based. First, in Section 7.2.1, the definition of each analysis aspect is described. Thefollowing sections briefly describe the individual techniques, structured according to theseanalysis aspects. Section 7.2.8 summarises the advantages and disadvantages of eachtechnique relative to the other techniques. Note that the descriptions and characteristicsof the techniques are based on literature studies, rather than on test results.

7.2.1 Analysis aspects

The following definitions are used for the evaluation of the positioning techniques. Someof the definitions deal with relative and absolute measures, because occasionally no hardabsolute facts could be found and instead the measure is compared relative to othersystems.

Accuracy: the relative or absolute description of the error between the position as com-puted by the technique and the ‘true’ position. The ‘true’ position is the positionto be measured and considered to be known.

Measurement time: the relative or absolute time that is required to present the positionto the mobile device, i.e. the ‘real-timeness’ of the positioning system.

Coverage: the measure for assessing the system’s capability to cover the area of interest.

Reliability: the measure for assessing the consistency of the system, i.e. whether thesystem is sensitive to changing environments, like moving people. It is assumed thatthe equipment itself does not fail.

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Costs: the relative or absolute costs of the entire system (if known), consisting of hard-ware and software investment, implementation costs, service, maintenance, and op-erational costs.

Data transfer: the ability of the system to provide data transfer necessary to link themobile device and the central computers.

Maintenance: the measure for effort and cost to keep the system up and running foroptimal functioning.

Outdoor and indoor capability: the ability of the positioning system to function bothindoor as well as outdoor. In addition, the implications to the performances of thesystem is described.

In the description of the positioning techniques, ‘mobile device’ refers to the actualdevice carried by the visitor, who should be able to take the device anywhere in thecoverage area. The positioning is based on the location of the mobile device itself.

7.2.2 WiFi

WiFi positioning technology works with fingerprint matching, based on signal strengths.When WiFi Access Points (APs) have been placed correctly one should be able to sensemultiple APs on every location in the coverage area. The device to be positioned acquiresthe individual signal strengths and sends it to a central server, which computes the positionby comparing the signal profile with a database containing signal profiles of the pre-surveyed locations. After the position determination the real-time position can be sentback to the mobile device. Usually, this technique is based on a signal frequency of either2.4 GHz or 5 GHz. This difference in used frequency causes differences in accuracy andcoverage, to be discussed in the corresponding analysis aspects.

Accuracy

The geometry of the distribution of the APs has a large influence on the accuracy. Inthe most ideal case, when the geometry corresponds to the distribution of Figure 7.2(a),decimetre-level accuracy can be achieved [54]. However, deviations from this ideal casecan decrease the accuracy level significantly. For example, a distribution as depicted inFigure 7.2(b), will reduce the accuracy level to 1-3 m.

Another important factor is the type of surrounding environment, because the char-acteristics of signal propagation are essential for computing the position. For example, abuilding consisting of small rooms separated by concrete walls will create such distinctivesignal profiles for all positions, that the accuracy levels of 1-2 m [30] can be achieved.Conversely, when the system is used in a open-spaced corridor or an empty hall, the sig-nal will not be attenuated in a unique way; an effect that is needed to distinguish uniquespots. An accuracy level in the order of 2-3 m [30] can be expected in these situations.Especially when signal reflections show irregularity over time (like water conditions), po-sition hopping can occur [30], resulting in significant changes in the measured position,without any change in the ‘true’ position.

When a 5-GHz signal is used instead of a 2.4-GHz signal, the attenuation of the signalincreases, resulting in a more distinct fingerprint. This implies that a better accuracy canbe achieved by using a 5-GHz signal rather than a 2.4-GHz signal.

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(a) Ideal geometry. (b) Example of a non-ideal geometry.

Figure 7.2: Geometry of the distribution of APs (after [54]).

Measurement time

WiFi positioning has a latency in the position determintation of the mobile device. This isdue to inability of the wireless network card of the mobile device to sense signal strengthsand to transmit and receive data at the same time. This latency is about 5-20 seconds [30],depending on the configuration of the wireless network card.

Coverage

The APs typically have a range of about 100 m when using a 2.4-GHz signal. When usinga 5-GHz signal the attenuation increases, resulting in a reduced signal range. The exactnumber of APs needed for the pilot area at the GeoFort depends on the implementationarea. Simulations of the pilot area provided by Lumiad (WiFi positioning company, seeSection 7.3.1) have revealed that the number of APs should be about 10 to cover the lowerfloor of building A. This is only a small part of the GeoFort, for the entire GeoFort moreAPs are needed for full coverage.

Reliability

The type of APs is important for the reliability of the system. The most importantproperty is the stability of the APs; drop outs and bugs in the firmware during operationneed to be avoided. Consumer APs meant for wireless networking are in general notsuitable for positioning, but APs with a good enough stability are commonly available.

The fingerprinting technique is sensitive to changes in the surrounding environment,such as opened or closed doors.

Costs

Costs of two companies have been assessed, Lumiad and Cisco Systems (Section 7.3).Both companies use the same type of APs, that cost about ¿400 each. The difference inprice for both companies lies in the required extra hardware and software. For Lumiad, the

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only extra hardware that is needed is a server capable of running the positioning engine.However, the positioning system of Cisco Systems needs a WLAN Controller of about¿13500 in addition to a server. The software of Ekahau is priced as approximately ¿9000,while Cisco Systems needs the software modules ‘Cisco Wireless Location Appliance’ of¿11000 and ‘Wireless Control System’ of ¿4000. These prices are based on licenses for50 simultaneously tracked devices.

One of the advantages of using WiFi for positioning is the tendency towards betterquality APs and positioning software, cheaper hardware and software, and a better un-derstanding of the APs and their signal profiling and propagation model. This is due tothe booming usage of Wireless Local Area Networks (WLAN).

Data transfer

A very important advantage of using WiFi is the combination of WLAN (which is requiredfor the GeoFort) and the positioning system in one infrastructure. The same APs thatare being used for positioning are also used for wireless networking.

Maintenance

The most important maintenance issue of the WiFi positioning system is the requiredsite surveying when changes in environment have taken place. For example, when doorsor high-density objects have been introduced in the scene, the signal strength profiles ofnearby locations are changed, resulting in possible erroneous position computations.

Outdoor and indoor capability

APs exist that are designed to operate outside. Although the price is somewhat higherthan for indoor APs, the coverage is generally better than for indoor APs (up to 100 min open space) and the data transfer can still be combined. However, achieving a goodaccuracy level is more challenging, because fingerprinting is more difficult. Since thesignals are not attenuated enough in open space to reach the same level of unique profilesas indoor, it is much more difficult to distinguish between individual nearby positions.According to [39], the accuracy will be in the order of 10-40 m outdoor.

7.2.3 RFID

Radio Frequency IDentification (RFID) as a positioning technique basically comes down tocell positioning of the mobile device. RFID can be either an active or a passive technique,although both rely on the same principle: the reader sends out a radiofrequency signalpulse which is uniquely transformed by the mobile device and sent back to the readerfor identification. Active RFID deals with an active tag attached to the mobile device,which actively sends back its unique tag information to the reader. This results in a largecoverage range, but also a tag that needs to be battery powered. The passive version relieson the radiofrequency power of the incoming signal to power the tag and to send back theunique tag information [46]. As a result, the passive tag does not need any power supplyother than the incoming signal and is significantly cheaper, but also limits the operatingrange significantly. Both versions of RFID can only compute the position in the cell ofthe nearest reader.

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Accuracy

RFID is a discontinuous positioning system, i.e. cell-based. Each RFID reader covers aparticular cell where the tag can be sensed. The basic RFID positioning systems can onlyachieve the accuracy in the order of their cell size. The more RFID readers are available,the smaller the cells and as a result the better the accuracy.

Measurement time

The time difference between the reader sending out its signal and the tag modulating thissignal and sending it back is negligible.

Coverage

Active RFID tags have a range of about 100 m, passive RFID tags have a much smallerrange of about 3 m or less. For both active and passive RFID systems, many tags needto be implemented: an active RFID positioning system needs many tags to create smallcells (overlapping areas of cells of different tags) for a better accuracy, a passive RFIDpositioning system needs many tags to cover the entire GeoFort.

Costs

RFID readers range from ¿21 to ¿3500 according to [49] and [10], all of them capable ofsensing both passive and active RFID tags. The large price difference is mainly relatedto the sensing range capability and simultaneous tag readings. For example, the ¿3500priced reader can detect 50 passive RFID tags simultaneously at a maximum distance of3 m while serving as WLAN AP, while the ¿20 reader is only capable of reading a passivetag within 12 cm of the tag. The prices of the tags vary between ¿10 to ¿100 for activetags and less than ¿1 for passive tags, where again the price difference depends on therange capability.

Reliability

Signal interference influences the reliability and occurs when multiple tags are being readsimultaneously; when both active and passive tags are being used operating at the samefrequency; or when other devices that use the same frequency are in the vicinity of thetags and readers [46]. Obstructions that (partly) block the signal limit the possibility forposition determination, in particular for passive tags.

Data transfer

WLAN-integrated RFID readers exists, however, these are rather expensive and not com-monly available. RFID readers can also be linked to a network using a suitable softwaremodule. This implies that a separate data transfer infrastructure needs to be imple-mented.

Maintenance

When using cheap passive tags, they probably need regular replacement. When usingmore expensive active tags, the need for battery replacement gives problems. Based oninformation on the active tags [48], the nominal battery life is at least 3 years. When

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high scan rates are required for high frequency position updates, it can be expected thatthe battery life decreases. However, the active tags could be integrated with the powersupply of the mobile device, making the battery obsolete.

Outdoor and indoor capability

In general the performance of the RFID positioning system is the same for indoor andoutdoor environments. However, for outdoor usage the placement of the readers is not atrivial task if a good accuracy is required, because many tags should be placed close toeach other.

7.2.4 Ultrasonic positioning

An ultrasonic positioning system uses travel time, or Time Of Arrival (TOA) [44] ofultrasonic waves from at least three transducers that transmit an ultrasonic pulse. Soundhas a much lower propagation speed than electromagnetic waves (as used for the othertechniques); this is indirectly the main reason for the increased accuracy. More detailed,TOA of electromagnetic waves requires an extremely stable and accurate clock, becausea clock error of 1 ns will result in 0.3 m error. However, when using the TOA of acoustic(pressure) waves a clock error of 1 ns will result only in a 0.3 µm error. The techniqueas described in [44] uses time synchronisation between the transducers and the receiverbased on a radiofrequency signal, and works with a constant transducer pulse frequencyof 40 kHz and a constant radiofrequency transmitter pulse frequency of 418 MHz.

Accuracy

A test showed that with the use of four ceiling-mounted transducers, one is able to achievean accuracy of 10 cm within the boundaries of a room and 25 cm at the boundaries of atest room of 4.2 m × 6.5 m [44].

Measurement time

The measurement time is very small compared to e.g. WiFi positioning. The system firstreceives the radiofrequency (rf) code for synchronisation, followed by transducer pulseslasting 30 ms each. When four transducers are used, the position is updated at 4 Hz (thisincludes the computation time). The ultrasonic positioning system can be modified tohave a higher position update rate if this is required, for example, for applications whenvisitors are running.

Coverage

The test presented in [44] used four transducers and one rf-transmitter for a room of4.2 m × 6.5 m. The same paper describes a successful extension to cover an area of10 m × 18 m by using six transducers placed 7 m above the ground. Larger configurationsare feasible, however they result in longer measurement times [44].

Reliability

The ultrasonic positioning system needs a clear line-of-sight between the transducers andthe receiver. When a transducer is blocked, the pulses of this transducer will reach the

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receiver through multipath. Since this has a negative effect on the accuracy, the receiveris programmed in such a way that it stops ‘listening’ after a certain amount of time (onlythe first cycle of the individual pulses are used [44]), so that multipath signals will not beregistered. As long as at least three transducers have a clear line-of-sight, the system isable to compute the position.

High-frequency sound interference such as jangling keys can disturb the positioningsystem [44].

Costs

The major costs of the system are those of the control unit (around ¿500) and receiver(around ¿300) [45]. The transducers and rf-transceiver are relatively cheap, a few euroeach [13].

Data transfer

The data transfer between the positioning system and the central computers is not cov-ered. This means that a WLAN should be implemented as data transfer network.

Maintenance

Since the receiving module for the ultrasonic and rf-signals is attached to the mobiledevice, the power can be combined with the power supply of the mobile device. No sitecalibration is required after changes in the environment, as opposed to WiFi positioning.

Outdoor and indoor capability

In principle, the system can be used both indoor and outdoor. One of the disadvantagesof indoor usage is that the signals do not penetrate objects such as walls, thus the systemis implemented on room-level. A disadvantage for outdoor usage is that more backgroundnoise is present and that the transducers have to be able to send the signal over largedistances, resulting in a worse accuracy and less reliability. In addition, there is a moreethical disadvantage related to the outdoor usage, namely the possible distressing ofultrasonic sound-sensitive animals, such as bats. With the ultrasonic positioning signalswhich are sent out at a frequency of 40 kHz [13], the positioning system transmits in thefrequency spectrum used by bats (20-120 kHz) [25]. Apart from bats, other animal speciescan also be sensitive to the frequency used. It is difficult to find out the pain and distressthreshold of the animals, however [13] describes that dogs for instance can perceive thesignals within 2.5 m and cats within 11 m of the source. Therefore it is very importantto be careful with the use of these transducers close to animals.

7.2.5 UWB

The basic principles of the Ultra-Wide Band (UWB) system are comparable with GlobalNavigation Satellite Systems (GNSS) ranging and positioning, making use of the traveltime of electromagnetic signals. UWB uses an extremely large frequency bandwidth forincreased accuracy. The UWB bandwidth is at least 500 MHz [24], but usually 1 GHz,with a central frequency of 3 GHz [28]. At least four transmitters are necessary forposition computation, three for the 3D position estimation (the pseudo ranges between

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the receiver and the known locations of the transmitters), and one for the estimation ofthe clock error. It is especially suitable for a short-range, accurate location system [24].

Accuracy

Under normal circumstances typical UWB positioning systems can achieve sub-metreaccuracy [56] and even centimetre accuracy [24]. Since the system is based on timedifferences, the clock stability and clock synchronisation between the nodes is one of themost important factors determining the accuracy. The accuracy is improved by using thelarge bandwidth of the UWB signals, because it offers high temporal resolution due tothe spreading of the electromagnetic signal, resulting in improved ranging accuracy. [24].

Measurement time

The measurement time is practically negligible, especially with the use of UWB receivers.

Coverage

The coverage depends on the frequency used and its bandwidth, since this influences theamount of absorption and attenuation. Since the central frequency of a typical UWBsignal is 3 GHz [28], the signal is able to penetrate walls and doors, similar to WiFisignals. Depending on the frequency, ranges up to 100 m can be obtained.

Reliability

The large bandwidth results in unequal propagation characteristics of the signal. Infact, the UWB system improves the reliability, because the different signal componentsincrease the probability that a part of the signal is able to bend around or penetrateobstructions. In addition, the very large bandwidth results in a decrease in spectral densitywhich reduces the interference of other wireless systems [24]. Disadvantages are multipathpropagation, non-line-of-sight propagation and accurate timing of ultra short pulses ofvery low power density. Using advanced techniques these problems can be reduced bysuboptimal, but practical solutions [24]. The complexity of the optimal schemes makesthe UWB system still an experimental system.

Costs

The components of the UWB system are not yet as extensively used as for example WiFi,resulting in more expensive and more experimental equipment. However, according to [24]the system is a low-power and low-cost implementation system, although no hard costestimations are given.

Data transfer

Theoretically, data transfer can be performed by the UWB system. However, practicalintegration is still lacking, therefore integration with WLAN is necessary.

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Maintenance

The system could be integrated with the power supply of the mobile device since onlya standard receiver is needed as well as a stable clock. In addition, no calibration ofthe site is required when objects are introduced or removed, unlike the WiFi system.The time-keeping and synchronisation should work accurately, implicating that eitherthe reference nodes should rely on one and the same (stable) clock; the clock should besynchronised via ranging between the reference nodes; or an extra reference node is usedfor the double-difference method.

Outdoor and indoor capability

The indoor environment requires a well-chosen bandwidth and central frequency to dealwith multipath, interference and propagation effects. UWB is suitable for outdoor usage,but the range is limited due to the low power of the UWB signal.

7.2.6 Bluetooth

A positioning system based on Bluetooth is not as well known as most of the other systemsdiscussed; it is still experimental. Only a few papers provide usable information about thistechnique. The system uses triangulation of electromagnetic signals at a centre frequencyof 2.4 GHz. This principle is similar to the technique used in UWB positioning.

Accuracy

The accuracy is in the order of metres and deteriorates with the range of the system [27].Tests presented in [27] showed that the average absolute positioning error is about 1.7 m.

Measurement time

The measurement time of the system depends mainly on the acquisition time of the linkbetween the transmitters and receiver, which usually is more than 5 seconds [27]. Thetest in [27] showed that the worst-case position determination time was 10.5 seconds.

Coverage

The typical range of the system is about 10 m to 100 m, depending on the power classof the equipment. The test as discussed in [27] showed that a usable signal strength wasonly available within 20 m of the Bluetooth source.

Reliability

The reliability is improved by the Bluetooth frequency hopping (1600 times per second)to reduce interference with other devices operating at a frequency of 2.4 GHz. In addition,the system can detect errors and restore corrupted data [27].

Costs

The costs of Bluetooth receivers and transmitters are relatively low due to the high marketavailability and the common usage in mobile devices, such as phones and PDAs. On the

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other hand, the software for position computation is rather exclusive and thus assumedto be expensive.

Data transfer

Bluetooth was originally developed for data transfer. The bit-rate is in the order of 1-3 Mbit/s, which is compared to WiFi relatively low, but still enough for transferring onlythe position data. Many mobile devices using the same Bluetooth WLAN simultaneouslyfor data transfer can result in a network overload.

Maintenance

The receivers could be integrated with the mobile device for power supply and the Blue-tooth reference nodes can be easily replaced due to the simplicity and low costs of thedevices (when simple off-the-shelf devices are used).

Outdoor and indoor capability

The system works indoor as well as outdoor. However, mounting of the devices in the out-door environment is challenging, in particular since the limited coverage of the Bluetoothdevices will result in a dense device distribution.

7.2.7 GPS

The Global Positioning System (GPS) is currently the most important GNSS positioningsystem. The system is based on measuring pseudo ranges between the receiver and thetransmitters using the TOA technique of electromagnetic signals.

Accuracy

The accuracy of coordinates obtained from single-frequency GPS receivers is about 15 m,depending on atmospheric conditions, multipath effects, obstruction, and availabilityof satellites. Possible enhancements are Differential GPS (D-GPS) techniques, offeringdecimetre level accuracy, or Real-Time Kinematic (RTK) techniques for centimetre levelaccuracy. A disadvantage of using D-GPS or RTK-GPS is that complementary servicesshould be available (such as a base station and a communication link between the providerand receiver). Moreover, it will increase the purchase costs of the receiver compared toa simple single-frequency receiver. When GPS is integrated with Galileo and the up-graded GLONASS, the accuracy is likely to improve, mainly due to the redundancy inmeasurements and improved ranging techniques.

Measurement time

GPS positioning devices have a cold start-up time of 12.5 minutes, when the navigationmessage (almanac) is not present or too out-dated. Assuming that the mobile devicesare used regularly, this time can be reduced significantly when using the last updatedalmanac. When the system is up and running, almost instantaneous position fixes (orderof seconds) can be obtained.

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Coverage

One of the disadvantages of this system is that it does not work autonomously, i.e.the positioning system relies on the availability of the GPS satellites. Usually around8 to 10 satellites can be sensed with a fairly good receiver and in ideal outdoor con-ditions. However, environmental obstructions like trees and buildings can reduce theavailability, resulting in a worse accuracy level or even outages.

Reliability

The reliability depends on the environment and the availability of satellites.

Costs

The receiver costs depend on its functionality and performance. There is a broad varietyof receivers available, ranging from relatively cheap to rather expensive. Base stations forD-GPS or RTK-GPS possibilities are much more expensive and should only be installedif significant higher accuracy levels are required.

Data transfer

Since the receiver is usually configured and built as a one-way (only receiving mode)device and not interoperable with any known wireless networks configurations, a WLANsystem should be combined in the mobile device. However, some current GPS devices arealready integrated with a WLAN device.

Maintenance

The constellation of transmitting devices (i.e. the satellites) is maintained externally. Asa consequence no attention has to be paid to this part of the system.

Outdoors and indoor capability

Outdoor usage is only restricted by the properties of the environment (e.g. forests asoposed to open fields). However, the indoor capabilities, and especially in the case of theunderground GeoFort buildings, are practically excluded. The thick soil on top of mostof the buildings makes the reception of the GPS signals virtually impossible. Thus, forthe indoor environment the technique should be integrated with e.g. WiFi, which is mostprobably also needed for the data transfer.

7.2.8 Technique trade-off

Table 7.1 shows the trade-off study of the positioning techniques described in Section 7.2.The WiFi positioning system is the most suitable technique for positioning. The mostimportant reason is that it provides a combination of positioning together with WLAN asa data transfer network, which is essential for the GeoFort. In addition, the coverage issatisfactory due to the use of electromagnetic signals of 2.4 GHz or 5 GHz. The downsideof this technique is that the purchase costs are relatively high, while the maintenance costsare variable, depending on the need to calibrate the system if changes in the environmentoccur. The room-level accuracy (in the order of metres) is assumed to be satisfactory

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for the GeoFort. For implementation at the GeoFort, WiFi should be integrated with atechnique that is more suitable for outdoor positioning.

The UWB system is also an interesting choice, however, since the system is still inan experimental phase and currently very expensive, it is less advisable for usage atthe GeoFort. In addition, the lack of real practice with the system makes it difficultto compare the practical performances with respect to the WiFi, ultrasonic and GPSpositioning systems.

If the WiFi positioning system is being used for the indoor parts of the GeoFort,having a room-level accuracy, an improvement of the accuracy could be achieved bythe implementation of ultrasonic positioning for the part of the indoor buildings wherea higher accuracy is desired. Since the system is rather cheap compared to the othersystems, and since the measurement time is relatively small, ultrasonic positioning is aninteresting and appropriate technique for implementation at the GeoFort.

Finally, GPS offers a good opportunity for a hybrid system with WiFi positioningfor outdoor usage. Using filtering techniques (such as Kalman filtering), the two tech-niques can be combined in order to achieve a better accuracy in the outdoor environment.Since GPS devices are currently widely available and relatively cheap, the extra effort forcombining GPS with indoor positioning techniques will be compensated by the improvedaccuracy and reliability outdoor. In addition, the low maintenance and consequentlysmall operational costs contribute to the positive advice of using this system outdoor. Itshould also be noted that in the future - possibly already at the time of implementationat the GeoFort - the services provided by the Galileo and GLONASS GNSS systems couldsignificantly improve the positioning accuracy and reliability.

Table 7.1: Trade-off study for positioning techniques. A ‘+’ means that the technique is performingsatisfactory, a ‘+/-’ means that the technique is performing moderately, and a ‘-’ minus sign indicatesthat the technique is performing poorly with respect to the particular analysis aspect.

Technique Acc

urac

y

Mea

sure

men

tti

me

Cov

erag

e

Rel

iabi

lity

Cos

ts

Dat

atr

ansf

er

Mai

nten

ance

Out

door

and

indo

orca

pabi

lity

WiFi +/- +/- + +/- - + - +/-

RFID - + - + +/- +/- +/- -

Ultrasonic + + +/- +/- + - + -

UWB + + + +/- - +/- +/- +/-

Bluetooth +/- - - - +/- +/- +/- -

GPS - +/- +/- +/- + - + -

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7.3 Market investigation

From the trade-off study in the previous section it is concluded that WiFi as a positioningsystem is currently the most appropriate option for the GeoFort. Several companiesexist that are specialised in (WiFi) positioning techniques. Section 7.3.1 gives a shortdescription of the most prominent companies in the field of WiFi positioning.

Apart from a positioning system, software needs to be developed for the user interfaceof the handheld, as well as for the central computer. Since the software has very specificrequirements, it is not likely that an off-the-shelf software module already exists. Thisimplies that a company needs to be addressed that is able to develop a software modulethat meets the requirements as described in Section 7.1.4.

7.3.1 Positioning systems

The following companies will be shortly discussed in this section: Ekahau, Lumiad, CiscoSystems, Newbury Networks, Ubiquicom, Place Lab, and Aeroscout. It is outside thescope of this report to explain the techniques used by each company, the reader is referredto the website of the company - which can be found in the reference list - for a moredetailed description.

Ekahau

Ekahau [15] is an international company, founded in 2000, that is currently recognisedas one of the leading companies in the field of WiFi positioning technology. Ekahau hasdeveloped specialised software, with which it is possible to track wireless devices such aslaptops, handhelds, and WiFi tags. Some key aspects of the Ekahau positioning systemare [15]:

� If a certain device makes use of the IEEE 802.11 standard it can be tracked;

� Ekahau software can be integrated with a third-party application (based on Java)or database to provide LBS;

� Ekahau supports all IEEE 802.11 APs;

� At least 3 APs are needed for the computation of the position of the device;

� In a typical multi-floor WiFi environment, the accuracy is 1-3 m, but more APs canimprove the accuracy;

� The WiFi tags developed by Ekahau only communicate about 60 Bytes per locationupdate, so a large number of tags can be used without significantly slowing downthe WiFi network. Furthermore, idle tags go into a sleeping mode in which they donot send or receive data at all.

� The client software can be downloaded for free, for use on a wireless device.

Ekahau distributes their system not only directly to the customer, but also via localcompanies. In the Netherlands, the Ekahau positioning system is distributed via thecompany Lumiad.

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Lumiad

Lumiad [35] is a small-scale Dutch company, specialised in the implementation of wirelessnetworks and the Ekahau WiFi positioning system. Furthermore, they offer service andconsultancy in the field of wireless networking. Due to the small scale of the company, theservice is personalised and customised to the wishes and requirements of the customer.

Lumiad was contacted to cooporate in the project, by implementing and executinga WiFi positioning system, based on the software of Ekahau. This pilot is discussed inmore detail in Section 7.4.

Cisco Systems

Cisco Systems [11] is a large international company, founded in 1984, that offers hardwareand software solutions for (wireless) networking. Cisco Systems is one of the world leadersin the development of IP-based networking. One of the products of Cisco Systems is theirCisco Location Solution, which includes APs, WLAN controllers, and the Cisco WirelessControl System. With this system, wireless devices such as laptops, WiFi tags, andhandhelds can be located, while having a wireless network via the same infrastructure.

Newbury Networks

Newbury Networks [41], ‘The Location Systems Company’, enables enterprises to locate,manage and secure applications running over WLAN. With Newbury Networks it is pos-sible to provide real-time positioning based on WiFi.

Ubiquicom

Ubiquicom [50] is an Italian company specialised in the tracking of people and assets.The object or person to be tracked has to be within the coverage of a standard WiFinetwork and equipped with a WiFi-enabled device. The company produces special RFIDtags for this purpose. Ubiquicom offers solutions in the field of logistics, manufacturing,healthcare, public areas, and retail.

Place Lab

Place Lab [43] is an organisation that has developed software for device positioning, whichworks with an integration of different techniques. Place Lab aims at providing indoorpositioning as well as outdoor positioning on a large scale. This is done by combining theuse of WiFi APs with GSM phone towers and fixed Bluetooth devices. These ‘beacons’have a known location and carry a unique ID, which is used for the position calculation:the IDs of the devices received at a certain position are used to estimate the position of thereceiver relative to the position of the beacons. Place Lab makes it possible to determinea position autonomously, without continuous interaction with a central service.

AeroScout

AeroScout [3] uses standard WiFi networks in combination with RFID to locate andmanage assets and people in multiple environments. Their main fields of application arelogistics, manufacturing, and healthcare, although they can also offer services related topublic spaces, government, and retail. AeroScout uses a combination of TOA and received

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signal strength of WiFi signals, making it possible to provide positioning systems for awide range of environments.

7.3.2 Software development - Geodan

Many companies are available that can deliver customised software modules for a widerange of applications. For the GeoFort project, the Dutch company Geodan [20] is ofparticular interest, because they can develop customised software for visitor tracking andtracing systems. An off-the-shelf software system that is currently available is GeodanMovida. This software module can be combined with positioning hardware to computethe position of a device. Movida supports position information that is acquired by eithercell-based positioning, choke-point visibility (i.e. a device is detected when it leaves orenters a certain area), or 3D coordinate positioning. Geodan states that organisationscan use Movida to:

� Identify and locate people or assets in real-time;

� Generate location-based information and alert-based messages;

� Generate extensive reports for further (statistical) analysis;

� Provide visualisation capabilities (including 3D).

Due to the limited time available for this project it was decided not to integrate Movidain the positioning pilot. Still, this software module can prove to be useful as a startingpoint for the visitor tracking and tracing application at the GeoFort. However, for theinterface requirements of the GeoFort significant modifications in the Movida softwarewill be necessary.

7.4 Pilot WiFi positioning system

The WiFi positioning pilot was implemented and executed in cooperation with Lumiad(Section 7.3.1). First, the preparation of the pilot is described. After that, the dataacquisition methodology during the pilot, the methodology of analysing the data, andfinally the discussion and interpretation of the results are presented.

7.4.1 Preparation of the pilot

The first step in the implementation of the pilot was planning the distribution of the APsand simulating the propagation of the WiFi signals to examine the coverage of the signals.This simulation was based on a preliminary 2D map of the site, with use of Ekahausimulation software. After this initial distribution plan, the practical implementationstarted with a site survey at GeoFort. The site was examined and mobile APs were placedaccording to the initial simulation. Two omni-directional antennas, one transmitting at2.4 GHz and the other at 5 GHz, were placed on a tripod, which was connected via a wireto the APs for data transfer to a laptop.

A laptop was used for the so-called site survey, a software module developed by Ekahau.This software module provides information about the signal strength of the individual APsover the entire pilot site, by walking around the site pinpointing the approximate position

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on the 2D map on the laptop. With this information, Lumiad examined the coverage ofthe APs and checked which APs provided proper signal strength for positioning, based ona receiving signal strength threshold of -75 dBm. It turned out that the initial simulationworked well and no major changes to the distribution of the APs were necessary. The nextstep was to check the signal coverage of the different frequencies. Lumiad concluded thatthe 5 GHz signal was more suitable for this site, because the larger signal attenuation atthis frequency creates more distinctive profiles per location, resulting in a better accuracy.

Based on the initial site survey, Lumiad installed higher quality equipment with thesame distribution. Each of these more expensive APs (around ¿400 per piece) consistsof antennas able to send signals on both the frequencies 2.4 GHz and 5 GHz, and localarea network cards for data transfer to the server. After the set-up of the APs and theserver, a new site survey was performed and the signal coverage was again examined. Thisnew site survey was based on a 2D map extracted of the 3D interior model (Chapter 6),which has an accuracy in the order of centimetres. The map included the nails that wereused for the interior modelling, of which the position is known very accurately. The nailsas indicated in the map can be used to assess the accuracy of the Ekahau positioningsystem. Therefore, a grid of 30 cells with dimension 50 × 50 cm was implemented in the2D map, see Figure 7.3. A position calibration database was created for the entire testsite (independent of the position of the grids), by walking around the site with a laptop,pinpointing at every metre the position on the 2D map. In this way, a database wasstored on the server with the corresponding signal strengths of the APs at every positionof the site. With this information, the Position Engine software of Ekahau, which wasrunning on the server, could compute the position of the laptop by comparing the signalstrengths as sensed by the laptop with the profile database on the server. If more signalstrength profiles are available per position, a more balanced position computation can beperformed.

Figure 7.4 illustrates the positioning technique of Ekahau. In step 1, the APs aresending out signals at a certain frequency of which the strength is sensed by the laptop,together with their ID. Next, the laptop transmits the signal strength profiles of theindividual APs to the server via the APs (step 2). When the information has beenreceived by the server, the server compares the strengths with the profiles in the databaseand estimates the position of the laptop. Subsequently, the server sends back the positioninformation to the laptop via the APs (step 3), which plots the position on the 2D mapon the screen of the laptop.

It was registered how much time was required for each step in the pilot implementation,to give an indication of the time required for implementation of a Ekahau positioningsystem:

� Initial survey: half a day;

� Installation of equipment: one day;

� Calibration, and configuration of the server: three days;

� Pilot execution: half a day.

Thus, for this pilot area size, 5 days in total were required for the WiFi pilot.

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Figure 7.3: 2D map of the pilot test site, combined with grids on three of the nails: 110 (bottom-right),112 (top-middle), and 113(top-left). The nails are indicated with a small triangle.

Figure 7.4: Principle of the Ekahau positioning system.

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7.4.2 Data acquisition methodology

The 2D map shown on the laptop contained grids positioned on top of three nails (nailnumber 110, 112, and 113). Each of the 30 square grid cells has sides of 0.5 m. Whilestanding exactly on one of the three nails, the grid number and the particular quadrantcharacter in this grid cell (denoted by ‘a’, ‘b’, ‘c’, or ‘d’) of the computed position wasrecorded, together with the time of measurement. The quadrant principle is clarified withFigure 7.5. Since the map is 2D and the calibration database was only based on this 2Dmap, height was not taken into account in this pilot.

Per nail, 50 measurements were taken per laptop: 25 measurements pointing to onedirection and 25 measurements pointing in the opposite direction, thus rotated 180◦ in theplane parallel to the ground. This was done to get an indication of the reliability of thesystem, since the signal strength profiles depend on the direction the laptop is pointingto. In a reliable system, both orientations should give approximately the same results.During the pilot two laptops were used (both doing the same measurements), thus havinga total of 100 measurements per nail. One laptop was rotated 90◦ with respect to theother laptop, so four orientations were covered per nail.

The performance of the WiFi positioning system was also tested for the corridor of thepilot test site. The main reason for this is that WiFi positioning normally performs worsein ‘open space’ (such as corridors) than in rooms. Figure 7.6(a) shows the map used forthe corridor test. The grid has square cells of 1 m. Another calibration database was madebeforehand for this test. During the experiment, the laptops were carried through thecorridor along the straight line between two nails, and stopped at each marking (everymetre, 31 in total) for measurements. At each of the markings the distance from the‘true’ position and its corresponding direction were visually estimated. This directionwas based on the bearing, i.e. N, NE, NW, S, SE, SW, E, W, according to the localorientation system as indicated in Figure 7.6(b). As a result the up, right, left, and downorientations would correspond to the N, E, W and S bearings, respectively. For each spot,3 measurements have been taken using two laptops, resulting in a total of 186 readings.

The third test of the pilot was a verification of the coverage of the system. In practice,this means walking around with the laptop and checking the coverage of the system onevery spot within the pilot area. If the system could not compute a position on screen,the corresponding area was noted down.

Figure 7.5: Measurement principle: example of computed position at the location of nail 113. Thesmall red triangle is the position of the nail and the green dot is the position as computed by the WiFipositioning system. The reading would be ‘10d’ in this case.

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(a) Full map. (b) Enlargement of thebottom part of the map.

Figure 7.6: The 2D map used for the corridor test. The enlargement shows an example of the ‘true’position (blue dot) and computed position (green dot). In this case, the reading would be 1 m, bearingNE.

7.4.3 Analysis methodology

After conversion from the grid measurement notation to actual coordinates, the com-puted positions and the ‘true’ position per nail have been plotted, with variable coloursindicating to the number of times the same position was computed. The origin has beenpositioned in the lower left corner of the grid, e.g. at the boundaries of 11b, see Figure 7.5.Next, the absolute deviations of the computed positions from the ‘true’ positions havebeen calculated. The mean of these absolute deviations is a measure for the accuracy ofthe positioning system, and will be referred to as the Average Positioning Error (APE)throughout the rest of this chapter. The precision is determined by computing the spread-ing of all observations per nail and orientation, independent of the ‘true’ position. Apartfrom these statistics, also the deviations over time have been plotted to see whether themeasurements converge to a certain value.

The second analysis is the corridor accuracy assessment. First of all, the actual coor-dinates per measurement location were computed according to the equations:

x = d · cos b,

y = d · sin b,

where x and y are the coordinates per measurement, d is the distance to the ‘true’ po-sition and b is the bearing expressed as an angle with respect to a fixed axis. With thisinformation the APE can be computed.

7.4.4 Pilot results and analysis

First the results of the grid measurements at the locations of the three nails will beconsidered, followed by a discussion of the results of the corridor measurements.

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Results of grid measurements

For the analysis of the pilot results a few aspects can be examined:

� The effect of the orientation of the laptop on the APE of the system;

� The difference in APE between the three nails;

� Whether there is a change in APE over time.

Table 7.2: APE and precision per orientation and per nail.

Orientation APE (m) Precision (m)

nail 110 nail 112 nail 113 nail 110 nail 112 nail 113

N 1.2 0.9 1.8 0.8 0.6 0.7

E 1.1 0.9 2.3 0.5 0.6 1.0

S 0.9 0.7 1.7 0.5 0.5 1.2

W 1.4 0.7 2.0 1.2 0.6 0.4

Overall 1.2 0.8 1.9 0.8 0.6 1.5

Table 7.2 shows that in general the APE and precision do not depend on the orientationof the laptop. However, between the nails clear differences can be seen. Figure 7.7(a)illustrates a plot of the computed observations and the ‘true’ position. The analysis ispresented for one nail only, but the corresponding plots of the other nails show comparableresults. Figure 7.7(b) shows a the deviations of the computed position over time. It canbe seen that there is no visible correlation between the elapsed time and the value of thedeviations. A boxplot is shown in Figure 7.8 to summarise the statistical parameters ofthe position computation deviations at nail 113. It can be concluded that the variationin position deviations is not large, which is confirmed by the small precision values inTable 7.2.

Since the difference in APE between the four orientations of one nail is not significantly,these measurements have been merged (thus having 100 measurements per nail) to assessthe difference between the nails 110, 112, and 113, independent of the orientation. Theseresults are indicated as the overall APE and precision in Table 7.2. The APE and precisionat nail 112 is significantly better as compared to the other two nails. Figure 7.9 illustratesthe distribution of the observations relative to the ‘true’ position, the deviations plottedover time, and the boxplot showing the statistical parameters of the measurements ofnail 110. The plot with the deviations over time illustrates that halfway during themeasurements the deviation increased, compared to the other measurements. This ismost probably an effect of people moving around near the laptop or in between the APsand the laptop, resulting in a change of signal strength profiles.

Figure 7.10 shows the plots based on the measurements taken at nail 112. It shows animproved APE and precision, since the observations are much more clustered, as indicatedby the colour bar. Moreover, it is clustered close to the ‘true’ position, which indicatesa better APE. The timeline with the deviations shows no particular tendency or patternover time, but indicates a lower average deviation, thus implying a better APE. The

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boxplot confirms the improved APE by having a lower median, lower quartile and upperquartile, indicating that the observations are closer to the ‘true’ position.

Figure 7.11 shows the plots of the measurements taken at the position of nail 113. TheAPE and precision at nail 113 are worse compared to the other two nails. The scatterplotshows a fragmented distribution of the observations, which indicates less precision. Theobservations are also relatively far away from ‘true’ position, compared to the measure-ments at the other nails, indicating a worse APE. The boxplot shows a significant tallerbox indicating a wide spread of the observations. The timeline shows an average deviationlarger than those shown in Figure 7.9 and Figure 7.10. The boxplot shows a relativelyhigh median and upper quartile and upper limit.

There can be several reasons for the difference in APE and precision of the mea-surements taken at the different nails. This difference can be caused by differences inperformance of the site survey, i.e. the fingerprints for a certain nail are more uniquethan for the other nails. Furthermore, nail 112, which showed a better APE and precisionthan for nail 113, can probably sense more different APs due to its position in the pilotarea (see Figure 7.3), resulting in a more distinct signal strength profile. This, in turn,results to a better APE and precision. However, further research should be carried out togive more insight in the cause of these differences.

(a) Scatterplot of the computed positions and‘true’ position of nail 110 in northern direc-tion. The colorbar indicates the number of po-sitions that were computed in the same quad-rant.

(b) Plot of the deviations over time of nail 112in eastern direction.

Figure 7.7: Scatterplot of the computed positions and ‘true’ position of nail 110 in northern direction,and plot of the deviations over time of nail 112 in eastern direction.

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Figure 7.8: Boxplot of the deviations of nail 113 in northern direction (the descriptions indicate thestatistical parameters).

Figure 7.9: Scatterplot of the distribution of the observations at nail 110, together with the ‘true’ positionin the upper-left corner, the deviations over time in the left lower corner, and the boxplot on the rightside. The red cross indicates an outlier.

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Figure 7.10: Scatterplot of the distribution of the observations at nail 112, together with the ‘true’position in the centre, the deviations over time in the left lower corner, and the boxplot on the right side.The red cross indicates an outlier.

Figure 7.11: Scatterplot of the distribution of the observations at nail 113 together with the ‘true’ position,the deviations over time in the left lower corner, and the boxplot on the right side.

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Results of corridor measurements

As described in Section 7.4.2, six measurements were taken at every metre along a straightline between nail 108 and nail 112, with the grid cell numbering starting at nail 108 andending at nail 112 (ranging from 0 m to 31 m). In the direction from nail 108 to nail112, three measurements were taken at each consecutive metre, so 32 × 3 measurementsin total. The same process was repeated in reversed direction, i.e. from nail 112 to nail108. These two sets of measurements are separately analysed to get an indication of theinfluence of the orientation of the laptop.

Figure 7.12: Plots with the mean deviations of the positioning in the corridor, which is variable accordingto the colour scale. The left-hand plot corresponds to the orientation nail 108 to nail 112, the right-handplot to the orientation nail 112 to nail 108. The colour bar and the number next to the point indicates thecorresponding deviation in metres, the red lines indicate the average direction of the deviations per gridpoint. The length of these lines corresponds to the deviation. Unfortunately, some lines are overlappingin the plot, but most of the deviations were in nothern direction.

Figure 7.12 shows the mean position deviations in both directions, based on threemeasurements per grid point. The colour bar and the number next to the point indicatesthe corresponding deviation in metres. In addition, the red lines indicate the averagedirection of the mean deviation per grid point. The grid points 7 to 15 have a high meandeviation, in particular for orientation 108 to 112. The average direction differs, butusually it is directed north. When going from nail 108 to 112, the deviation ranges from0 m to 11 m, while the deviation ranges from 0 m to 4.33 m when going from nail 112 tonail 108. A possible reason for this difference is that the fingerprint obtained during thecalibration with the site survey for nail 108 to nail 112 is not as distinctive as for nail 112to nail 108.

For the combined measurements in one direction, the APE and precision are sum-marised in Table 7.3. The worst position error experienced throughout the test site is11 m, which is relatively high, since the a priori position error was estimated at 1-3 m.

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As can be seen in Figure 7.12, position errors in the order of 6 m to 10 m have frequentlyoccurred during this experiment, indicating that the APE is worse than expected priorto the pilot. Better calibration data or a different AP distribution could improve thepositioning performance at these locations.

Figure 7.13 shows the distribution of the mean deviations over the grid line betweennail 108 and nail 112. Remarkable is the peak at 10 m and the dip at 22 m for orientationnail 108 to nail 112 and the dip at 10 m and the peak at 22 m for orientation nail 112to nail 108. From this data one could deduce that perhaps the site survey has beenperformed only in one particular direction; this is however not the case. Further researchshould focus on the changing variables of the system, and on the conditions during theexperiments.

Table 7.3: APE and precision per orientation in the corridor. The APE and precision have been calculatedin the same way as for the grid measurements analysis.

Accuracy (m) Precision (m)

Orientation Orientation Orientation Orientation

108 → 112 112 → 108 108 → 112 112 → 108

3.0 1.8 1.4 1.3

Figure 7.13: The mean deviations (APE) per orientation in the corridor.

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7.5 Pilot ultrasonic positioning system

This section describes the preparation of the ultrasonic positioning pilot, the methodologyof data acquisition, the performed analysis, and the results of the pilot.

7.5.1 Preparation of the pilot

The pilot was implemented by Dr. Cliff Randell, who proposed a low-cost indoor posi-tioning system [44] based on a combination of ultrasonic pulses and radiofrequency (rf)signals (see Section 7.2.4). One day was needed to set up the system at the GeoFort, halfa day to test the system, and a few hours to unmount the system again.

Prior to the system setup, a measurement grid was set out and painted on one quartileof the floor of the room were the system would be installed. With aid of a laser distancemeter the grid dimensions and coordinates were determined with an accuracy in theorder of centimetres. This high accuracy was required to test the ultrasonic positioningsystem, of which its accuracy was expected to be in at a centimetre to decimetre level.Figure 7.14 shows the 2D map of the entire pilot test site (building A) as was used for theEkahau positioning system, together with an enlargement of the particular room wherethe ultrasonic positioning pilot was implemented. As can be seen in 7.14, two sides areadjacent to parts of walls and door openings. The door openings are 2 m in height. Theintersections of the grid lines are considered to be the ‘true’ positions of these locationsin the room. For simplicity, the z-axis has not been included in Figure 7.14, however thepositioning is done in 3D. Since the coordinate system used is a right-handed coordinatesystem and the origin is located on ground level, the z-axis points upwards being zero onthe floor and increasing towards the ceiling.

Figure 7.14: 2D map of building A with an enlargement of the test site for the ultrasonic positioningsystem. The grid together with the local coordinates system has been used for analysis.

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For the setup of the system, four small transducers were attached to the ceiling, allpositioned 2 m from each other. Figure 7.15 shows an example of one of the transducers,the process of mounting this transducer on the ceiling, and the control unit. After themounting of the transducers and the installation of the control unit (that incorporatesthe rf-transmitter as well), the receiver software was calibrated to the positions of thetransducers. For the pilot a PDA was used for the position calculation, that was connectedto a hardware module (small box of 3 cm thickness) for receiving the ultrasonic and rf-signals.

Figure 7.15: Example of an ultrasonic transducer that transmits the ultrasonic pulses (left), mountingthe transducer on the ceiling (middle) and the control unit (right).

7.5.2 Data acquisition methodology

To make proper comparisons between the ‘true’ positions and the observations (the mea-sured positions), first the origin of the coordinate system of the ultrasonic positioningsystem was calibrated to the origin of the grid, so that the two origins coincide. Note thatthe orientation of the axes of both coordinate systems was still different. After this cali-bration, the nine intersections of the grid have been measured at three different heights,resulting in a total of 27 measurement points.

As can be seen in Figure 7.16, ‘A’, ‘B’, and ‘C’ indicate the different height levels, thenumbers 1-9 correspond to the nine grid points, so each number has a constant x- andy-value. For a stable placement of the device, a tripod has been used for the height levelat 1 m. However, this tripod was not able to reach the 2 m, but maximally 1.75 m. Inaddition, the tripod was not able to be placed against the walls so in these cases one hadto hold the device at the right position against the wall (where it was possible to positionthe device at 2 m). As a result, some measurements at the top level were taken at 1.75 mand some at 2 m. Table 7.4 shows the coordinates per grid point. An offset of 3 cm isadded to the z-coordinates, because the actual receiver was on top of the 3 cm thick box.

The individual measurement computations and storage were both carried out by thePDA. Each measurement lasted for approximately 2-3 minutes at 4 Hz, resulting in about700 observations per measurement point.

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Figure 7.16: The grid that was used for the measurements. The origin is indicated by O in blue; theorientation of the axes is illustrated by the green arrows pointing in the positive direction. Note thatthe solid wall is now located adjacent to the plane containing grid points 7-8-9 and the door opening issituated in the plane containing grid points 3-6-9.

7.5.3 Analysis methodology

First, the data files with the observations per grid point were linked to the corresponding‘true’ positions and the grid point coordinates were transformed to the local coordinatesystem of the positioning system. Subsequently, the 3D deviation vector was computed,based on the x-, y-, and z-deviations (distances between the computed positions andthe ‘true’ positions) per single grid measurement to the corresponding grid point. TheAPE has been determined by calculating the mean of the absolute deviations betweenthe coordinates of the observations (the computed positions) and the ‘true’ position; theprecision by computing the mean of the absolute deviations of the coordinates of theobservations itself to the mean coordinate of the observations (so independent from the‘true’ position).

The following aspects of the positioning system are analysed:

� Differences in APE and precision between grid points;

� Differences in APE and precision between height levels;

� Distribution of the deviations over time.

During the measurement campaign, a more or less constant offset in x- and y-directionswas noticed. It will be analysed whether the performance of the system improves whenthe data is corrected for the constant offset. In addition, the effects on the performancewill be analysed and discussed when the z-coordinates are not taken into account. This isimportant, because in general the APE in z-direction is worse than in x- and y-direction,since the transducers are located in the same plane.

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Table 7.4: Coordinates per grid point number. Note the sometimes changing z-coordinates at the threeheight levels.

Point number Bottom level (A) Middle level (B) Top level (C)

1 (0,0,0.03) (0,0,1.03) (0,0,1.78)

2 (0,1.25,0.03) (0,1.25,1.03) (0,1.25,1.78)

3 (0,2.5,0.03) (0,2.5,1.03) (0,2.5,2.03)

4 (0.725,0,0.03) (0.725,0,1.03) (0.725,0,1.78)

5 (0.725,1.25,0.03) (0.725,1.25,1.03) (0.725,1.25,1.78)

6 (0.725,2.5,0.03) (0.725,2.5,1.03) (0.725,2.5,2.03)

7 (1.45,0,0.03) (1.45,0,1.03) (1.45,0,2.03)

8 (1.45,1.25,0.03) (1.45,1.25,1.03) (1.45,1.25,2.03)

9 (1.45,2.5,0.03) (1.45,2.5,1.03) (1.45,2.5,2.03)

7.5.4 Pilot results and analysis

Figure 7.17 shows the scatterplots of the observations and the corresponding ‘true’ posi-tion. Grid point numbers (also indicated as control points) 1A, 5A, and 9A are selected,because these points are positioned gradually towards the walls. Control point 1A showsa distribution that is scattered in all directions, while control points 5A and 9A primarilyshow that the observations are scattered more in a distinct direction. A possible expla-nation for this effect is that for control point 1A - which is in the center of the room - thetransducer geometry changes occasionally (since only the three nearest transducers outof four are used), resulting in positioning errors in different directions. For control point5A and 9A, the transducer geometry is fixed, resulting in positioning errors mainly in adistinct direction. The deviations plotted over time in Figure 7.17 indicate that especiallyat the fresh start of the measurements the deviations are relatively high and subsequentlyconverge to lower values. Table 7.5 shows the APE and precision per control point andper height level. The APE and precision related to control point 1A are significantlyworse than those of the other control points. This is most probably due to the changesin selected transducer geometry. The observations at control point 5A showed the bestperformance, considerably better than the performance at control point 9A. The mostlikely reason for the improved APE at point 5A compared to 9A is the absence of thewalls, which could introduce interference effects. In general the APE and precision arebetter for level B than for level A and level C. Compared to the APE per point, the APEper height level is better. There is not yet a plausible explanation for this effect.

It was expected that the APE in the z-direction is worse than in the x- and y-directions.This is confirmed by the calculated APE and precision in Table 7.5 and Table 7.6. TheAPE is slightly improved if the data is corrected for the observed offset. Figure 7.18shows the same plots as in Figure 7.17, now applied to the offset-corrected data. Theobservations are now closer to the ‘true’ position.

Table 7.6 shows the overall APE and precision, based on all observations. It canbe concluded from this table that the elimination of the z-value improves the APE andprecision considerably and that the offset correction also slightly improves the APE.

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Figure 7.17: Three scatterplots of the observations with the corresponding ‘true’ position of the controlpoints 1A, 5A, and 9A. The plot in the lower-right corner shows the deviations over time per controlpoint.

Table 7.5: APE and precision per control point (1a, 5a, or 9a) and per height level (A, B, or C), variablewith the inclusion or exclusion of the z-coordinate and the offset correction.

APE (cm) Precision (cm)

Offset corrected z-value included 1a 5a 9a 1a 5a 9a

No No 36.9 18.2 26.5 28.0 2.7 4.6

No Yes 43.5 30.0 44.2 34.8 4.3 6.0

Yes No 30.8 11.4 19.9 28.0 2.7 4.6

Yes Yes 40.9 12.3 22.4 34.9 4.3 6.0

A B C A B C

No No 22.0 8.4 11.5 9.7 3.3 6.3

No Yes 34.1 29.9 38.5 11.6 11.8 25.4

Yes No 15.8 7.1 16.5 9.7 3.3 6.3

Yes Yes 18.7 18.7 35.9 11.6 11.8 25.4

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Figure 7.18: Three scatterplots of the observations with the corresponding ‘true’ position of the controlpoints 1A, 5A and 9A, based on the offset corrected data. The plot in the lower right corner shows thedeviations over time per control point.

Table 7.6: APE and precision of the ultrasonic positioning system.

Offset corrected z-value included APE (cm) Precision (cm)

No No 14.0 6.4

No Yes 34.2 16.3

Yes No 13.1 6.3

Yes Yes 24.4 16.3

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7.6 Comparison between WiFi and ultrasonic posi-

tioning system

In Section 7.6.1 a comparison will be made of the performance of the WiFi and ultra-sonic positioning system, followed by a comparison of the functionality of the systems inSection 7.6.2. Finally, in Section 7.6.3, a comparison of the costs will be given.

7.6.1 Comparison of APE and precision

For the comparison between the WiFi and ultrasonic positioning systems, the z-valueswill be taken into account (since the ultrasonic positioning system delivers real 3D coor-dinates), and the offset-corrected data is used. The reason for taking into account theoffset-corrected data is that an apparent constant bias was observed in the data and as aresult one would get a wrong impression of the performance. This bias is most probablydue to a small error in calibration of the origin of the two coordinate systems (of thetransducers and of the grid).

Comparison of the ranges of APE and precision

Ekahau WiFi positioning system: from Table 7.2 it follows that for the observationsper different orientation, the APE is between 0.7 m and 2.3 m and the precision isbetween 0.4 m and 1.2 m; for the corridor (Table 7.3) the APE is about 2.5 m andthe precision is about 1.4 m.

Ultrasonic positioning system: Table 7.5 and Table 7.6 show that the APE is between12 cm and 41 cm and the precision is between 4 cm and 35 cm.

These statistics indicate that the overall APE and precision of the WiFi system is inthe order of metres and the APE and precision of the ultrasonic system in the order ofdecimetres. In other words, the WiFi system can compute positions on room level, whilethe ultrasonic system can accurately compute positions within rooms and corridors.

Comparison of extreme outliers in APE

Ekahau WiFi positioning system: extreme outliers were experienced during the mea-surements in the corridor. The largest deviation from the ‘true’ position was 17 min the middle of the corridor. In addition, multiple times deviations of 9 m to11 m were observed. For the measurements at the three individual nails, the largestdeviations were about 3 m.

Ultrasonic positioning system: the most extreme deviation is 4.9 m, which is ex-tremely large for this system. This outlier is one of the observations at controlpoint 9A, see Figure 7.18. Since deviations with this order of magnitude are excep-tional, and the position is updated multiple times per second, these outliers couldbe filtered out easily when the software is modified correctly.

The WiFi positioning system produces the largest absolute deviations from the ‘true’position. The extreme deviations of the ultrasonic system are lower but relatively largecompared to the mean deviation. Software optimisation can possibly reduce or eveneliminate these outliers.

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7.6.2 Comparison of the functionality

The functionality parameters to be compared are the system setup time and effort, theposition presentation to the user, the latency in positioning, and the implications forimplementation of the positioning system.

System setup time

Ekahau WiFi positioning system: the total setup was four and a half day, includingthe initial site survey, setup and the calibration. Most time was spend on thecalibration, which had to be done several times for improved APE.

Ultrasonic positioning system: It took one entire day to implement the system. Themounting and clearing of the equipment took less time, about one hour in total, butthe calibration, debugging the software and optimising the system required by farthe most of the time.

The implementation of the ultrasonic positioning system was easier and took less timethan the implementation of the Ekahau system. This is also due to the much smaller testsite used for the pilot. The advantage of the Ekahau system of spending more time on theimplementation is that the APE is very likely to improve due to a more balanced databaseof signal strength profiles at each position. The disadvantage of this system is that thedatabase should be updated when major changes in the site occur. The advantage of theultrasonic system is that the easy mounting of the transducers and plug-and-play controlunits saves much time and makes the system easily adjustable. The main disadvantageis that professional help is required when the system is not calibrated correctly or whencorrections in the software are needed.

Comparison of the latency

Ekahau WiFi positioning system: most of the time the system was able to determinethe position within 3 seconds, which is less than expected prior to the pilot. If thesystem needed more time for position determination (in extreme cases more than aminute), this was due to a problem with the software on the server and not due tothe positioning technique itself.

Ultrasonic positioning system: the latency of the ultrasonic positioning system is,compared to the WiFi system, very small. The system computes the position at4 Hz.

Besides the fact that a system with a very small latency ensures the ‘real-timeness’experience to the visitor, an advantage is that filtering and averaging techniques can beapplied to improve the APE, while still having an acceptable latency. For instance, forthe ultrasonic positioning system, taking the mean of four computed positions once persecond to produce a much more stabilised position calculation is achievable. Furthermore,constraints like the maximum velocity of a moving receiver can be implemented very easilyto filter out anomalies. These possible filtering and processing techniques are currentlynot included in the system, but could be implemented.

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Position representation

Ekahau WiFi positioning system: the position was presented as illustrated in Fig-ure 7.19, where the green dot illustrates the position of the particular laptop itselfand the blue dots the positions of the other laptops. For the Ekahau Client (installedon the mobile devices), this representation differs only in that the other laptops andtheir position are not visible.

Ultrasonic positioning system: contrary to the WiFi system, the display did not in-clude any kind of map, only a blank field in which the position was plotted withrespect to the geometry of the transducers. Figure 7.20 shows the PDA with theposition on the display.

The advantage of the Ekahau representation of the positions is that one can see its positionon the 2D map of the area. The ultrasonic positioning system did not include this featureyet, but referencing the position to a (3D) map can be implemented.

Figure 7.19: The presentation provided by the Ekahau Positioning Engine on the laptop (position ingreen and the other connected laptops in blue).

Figure 7.20: The visualisation of the position provided by the ultrasonic positioning system. The blackdot represents the position, the blank rectangle around it represents the boundaries of the area coveredby the positioning system relative to the transducers.

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Implications for implementation of the system

Other important aspects of a positioning system are the implications and consequencesof implementing the system. Examples are the power consumption of the system, themounting of the devices, and aesthetical effects. Since not much is known about thepower consumption, no estimates can be given, only that it is likely that the WiFi systemwill consume more power than the ultrasonic system.

Ekahau WiFi positioning system: during the pilot, the APs were mounted on tripodsto keep them mobile. When the system is actually implemented the devices could beplaced on standards or (partially) attached to the ceiling. The antennas should benot obstructed for proper propagation of the signals. When placing the APs flat tothe wall, the visual effects can be minimised. Wires could be concealed by placingthem together with the wires of the lights. One should be careful with drillingholes in the wall for mounting the APs, since the GeoFort is a monumental site.In addition to the APs, a room or storage space will be necessary to accommodatethe central server for running the Positioning Engine. This server could be placedanywhere and is not bounded to the distance to the APs other than that the APsshould be connected to the server by a wire.

Ultrasonic positioning system: the transducers of this system are small and moresubtle. When the wires are placed together with the wires for the lights, the trans-ducers will probably not be noticed by visitors. However, the controller includingthe rf-transmitter will require more space and has to be placed in a similar way asthe APs. There is no central server necessary for running the system, so no placehas to be reserved to for a central device.

7.6.3 Comparison of costs

The costs are estimated for the indoor environment of the GeoFort, i.e. for all buildings.No manpower to implement the system has been taken into account, thereby it is assumedthat both require approximately the same amount of implementation workload. Thehandheld device the visitors will carry is also not taken into account, because this deviceis not different for both systems. The cost estimations are based on 250 simultaneouslytracked visitors.

Ekahau WiFi positioning system: it is estimated that approximately a hundred APswill be required to cover all buildings of the GeoFort. The APs are in the orderof ¿400 each. In addition to these costs, the most expensive part of the Ekahausystem is the Positioning Engine software, which is based on the number of trackeddevices. For 250 tracked devices the Positioning Engine will cost ¿38500.

Ultrasonic positioning system: a cost estimation for the ultrasonic positioning sys-tem was given by Dr. Randell. For an ultrasonic / rf-transmitter module withtransducers, cable and connectors for provision of 1D position sensing in RemiseA2 and corridor, and for 3D positioning in three rooms of Remise A1, the totalcosts are ¿1100. It is estimated that for the entire GeoFort, this infrastructurewill cost ¿10000. In addition, the required receivers for ultrasonic signal receptionand position calculation costs ¿310 each. There are 250 of these receiving devicesrequired.

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Table 7.7 gives an overview of the costs for both systems. The implementation costsfor both systems have the same order of magnitude. These costs do not include anyoperational costs. The difference in operational costs for both systems depends on howmuch professional help is needed in case of malfunctioning of the system, or when thesystem needs to be recalibrated. For the Ekahau system, recalibration of the systemand replacement of APs are expected to contribute most to the operational costs. Forthe ultrasonic system, the replacement of the special hardware components is the maincontribution to the operational costs.

Table 7.7: Cost estimations per positioning system, based on positioning in all buildings and 250 simul-taneously tracked devices. Note that for both estimations, no discounts and possible reductions on thesystem have been taken into account.

Component Price per piece Quantity Total price

Ekahau APs ¿400 100 ¿40000

Licences ¿38500 1 ¿38500

Total ¿78500

Ultrasonic Infrastructure ¿10000 - ¿10000

Receiver ¿310 250 ¿77500

Total ¿87500

7.7 Conclusions

Two key requirements are essential for the tracking and tracing system:

� A data transfer network should be implemented in the system, since the (position)information about the visitors should be directly available at a central computer onthe GeoFort;

� The tracking and tracing system must be a basis for providing LBS. Thus, theminimal required accuracy should be at room-level.

Currently, two positioning techniques are of interest for indoor implementation at theGeoFort: WiFi positioning and ultrasonic positioning. A WiFi positioning system shouldprovide the basis for indoor positioning, mainly because data transfer is necessary anywayand this infrastructure can be modified relatively easy to provide positions as well. WithWiFi acting as the basis for positioning, other techniques can be integrated to providea better accuracy for particular areas. One of these techniques is GNSS positioning toprovide the necessary improved accuracy outdoor, since the WiFi system will most prob-ably not meet the accuracy requirement outdoor due to problems with obtaining propersignal strength fingerprints in open space. Ultrasonic positioning can be integrated indoorfor reaching sub-room accuracy. It is especially suitable for small indoor environments,like rooms and remises. However, when this ultrasonic system is being used, ultrasonicreceivers should be bought, resulting in significantly increased purchase costs.

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Since WiFi-based positioning was selected to be the main positioning technique, thecompany assessment was carried out only for companies providing this kind of technique.Most companies offer the same kind of services and techniques, but only from Ekahau(and Lumiad) and Cisco Systems a cost estimation was provided. Ekahau turned outto be the least expensive solution for implementing the system at the pilot test site. Inaddition to these companies, another company should be selected to develop and offerthe essential software that links the raw position of the visitor to the LBS. This softwareshould be developed in cooperation with the GeoFort, in order to provide customisedsoftware that meets the requirements of the GeoFort.

Based on the analysis of the results of the WiFi and ultrasonic positioning techniquesthe performances have been determined for both systems. For the Ekahau positioningsystem, based on WiFi signal strengths, the Average Positioning Error (APE) was in theorder of metres. However, in the open-space corridor the performances varied considerably,having an overall APE of 2.4 m and outliers of 10 m to 17 m. In rooms and remises theoverall APE improved to about 1.3 m and a precision which was better than the precisionin the corridor (1 m as opposed to 1.4 m, respectively). For the ultrasonic positioningsystem, based on the travel time of ultrasonic pulses coming from transducers, the APEand precision were considerably better. The overall APE and precision were 24 cm and16 cm respectively. Thus, this APE and precision are roughly a factor ten better thanthe WiFi performance. However, the drawback of the ultrasonic system is that a datatransfer network is not integrated in the system (in contrast to the WiFi positioningsystem), and the usage outdoor is difficult. A more practical problem of the ultrasonicpositioning system is that it is not a commercial system, and consultancy depends on theavailability of Dr. Randell, situated in Bristol, UK. Expertise is easier acquired for theWiFi positioning system, since Lumiad, who implemented the Ekahau positioning system,is located in the Netherlands. The costs of both systems are comparable. Based on thiscomparison, the recommendation is to have the WiFi positioning system serving as themain positioning system, with GNSS as augmented system outdoor and the ultrasonicsystem for particular areas indoor.

Since both systems are booming in usage, the future prospects are optimistic andinnovations and optimisations can be expected. Furthermore, it is expected that hardwareand software costs will reduce in the future.

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Chapter 8

Conclusions

During this project a basis is laid to provide visitors of the GeoFort an interactive ‘geo-information experience’. This basis consists of geo-information which can be used for thisexperience and an investigation of the possibilities to obtain the position of the visitorsin real-time. The objectives and results achieved are summarised for the four differentproject parts: the 3D historical model of the GeoFort; the 3D model of the currentsituation of the fortress; the 3D model of the interior of the GeoFort; and the designof a real-time visitor tracking and tracing system. This chapter briefly recapitulates theconclusions of these project parts.

8.1 3D historical model of the GeoFort

The objective was to create a historical model that can serve visualisation needs, as wellas serve as a basis for further spatial analysis. This has been achieved by manuallydigitising photographs of historical maps using CAD software. Two models were created:one derived from a design map of 1885, and one from a map of 1904. To quantify thequality of these historical models, the current model of the fortress was compared to anorthophoto of 1940, and subsequently the orthophoto of 1940 was compared to the modelof 1904. The differences between common points in these models are in the order of afew metres. These differences are caused by the accumulated effect of digitisation errors,natural changes of the fortress, and a non-uniform distribution of sample points. Thefollowing can improve the 3D models:

� Addition of realistic textures to objects;

� The use of more information sources (e.g. point numbers of the parcels with coor-dinates) for a more accurate representation;

� Proper scanning of the analogue maps.

Furthermore, future work should include the implementation of information attributesto the buildings and storage of the different states of the fortress in a structured way tofacilitate comparisons between the different historical states.

8.2 3D model of the current situation

The constructed 3D model of the current situation of the fortress needed to be a basis forvisualisations, comparison with the historical status, and for planning and construction

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purposes. This was achieved by creating a model using a combination of RTK-GPSmeasurements, tacheometric measurements, and existing AHN LIDAR data. Althoughthere are some systematic shifts between these two datasets, an accurate model includingbuildings and terrain with metre-level accuracy has been derived. Texture mapping hasbeen applied to give the buildings a more realistic appearance. Future work includesthe completion of the tacheometric measurements for some of the buildings, since notall buildings are measured yet, due to the limited time available. Furthermore, it isrecommended to use aerial photogrammetry for the GeoFort, since accurate DTMs withrealistic textures can be derived using this technique.

8.3 3D model of the interior of the GeoFort

The objective was to create a photo-realistic model of one of the buildings of the GeoFort.To achieve this, two techniques were combined: tacheometry and laserscanning. Tacheo-metry was used to create an accurate vector model with a low amount of detail. To addmore detail to this model, point coordinates from laserscanning point clouds were added.Four models are created. A point cloud model gives the most detailed and accurategeometric representation of the actual situation. A 3D vector model provides accuratedimensions of the building which can serve for construction and interior design purposes.A 2D map is created and used as a basis for the tracking and tracing system. A 3D modelwith faces and mapped textures is created to use for visualisations to the public.

The accuracy of the model is very close to the natural roughness of the building,therefore, it is not advised to focus on improving the accuracy of the model. More detailcan be added to the model by manually processing more laserscanning data. Furtherresearch could be performed on the integration of the 3D model with a 3D tracking andtracing system for the GeoFort.

8.4 Design of a real-time visitor tracking and tracing

system

The design of the tracking and tracing system started with a determination of the require-ments for the positioning system, the data transfer network, and the handheld receiverand its interface, followed by a technique trade-off. From this, two positioning techniqueswere selected: WiFi positioning and ultrasonic positioning. For both systems a pilot hasbeen executed, to assess the quality and reliability of the systems. For the WiFi posi-tioning system, the Average Positioning Error (APE) was in the order of metres. Forthe ultrasonic positioning system, the APE and precision were significantly better, in theorder of centimetres (thus, this APE and precision are roughly a factor ten better thanthose of WiFi). However, the drawback of the ultrasonic system is that a data transfernetwork is not integrated in the system (in contrast to the WiFi positioning system). An-other disadvantage is that the ultrasonic positioning system is not a commercial system,implying that the availability of the system and consultancy is uncertain. The costs ofboth systems are comparable. Based on this comparison, the recommendation is to havethe WiFi positioning system as the main positioning system, augmented by GNSS foroutdoor positioning and ultrasonic positioning for indoor locations where a higher APEis needed than is currently possible with WiFi positioning.

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8.5 This project as a basis for the GeoFort

All three models provide a solid basis to develop GeoFort’s future attractions and exhibi-tions. The resulting models can be published on a website to raise public interest. It canalso be used for further planning on the construction of the GeoFort. Combined with atracking and tracing system it already creates a public attraction for the GeoFort. Withthe geoproducts created in this project, interesting geo-information-based attractions canbe built. The generated datasets offer many different possibilities for attractions.

The project goals have been achieved, thus providing a basis with which the GeoFortcan be further developed, in the end to become the best measured part of the Netherlands.

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Acknowledgements

The authors would like to thank the supervisors, Edward Verbree, Frank Kleijer, TjeuLemmens, Kourosh Koshelham, and Theo Thijssen, for their help and feedback during theproject. We also thank Bart Bennis and Willemijn Simon van Leeuwen for their helpfulfeedback and for their support at the GeoFort.

We thank the companies Arcadis, Grontmij, Ingenieursbureau BCC, IngenieursbureauPasse-Partout BV, and Lumiad for their services and expertise during the project. In ad-dition, we thank Dr. Randell from the University of Bristol, who offered a demonstrationof his ultrasonic positioning system at the GeoFort.

Apart from the companies, thanks go to Raymond van Uppelschoten from Staatsbos-beheer for providing his documentation of the GeoFort, and Gerco Meijer of Bunker Qfor his help with interpreting the historical maps.

Finally, thanks go to Martijn de Milliano for reviewing large parts of this report andgiving feedback on the structure and contents.

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