Approximated Environment Features WithApplication to Trajectory Annotation

Sebastian FeldMobile and Distributed Systems Group

LMU MunichMunich, Germany

Email: sebastian.feld@ifi.lmu.de

Martin WernerMobile and Distributed Systems Group

LMU MunichMunich, Germany

Email: martin.werner@ifi.lmu.de

Claudia Linnhoff-PopienMobile and Distributed Systems Group

LMU MunichMunich, Germany

Email: linnhoff@ifi.lmu.de

Abstract—Indoor location-based services is an ever emergingfield of research and application. In particular indoor navigation,i.e. positioning and route planning in freely walkable space, getsmuch attention. The philosophy of this paper is to incorporateisovists as a numerical representation of the local environmentto enable new types of indoor LBS. This paper has got two maincontributions. First, we define discrete isovists as an approxima-tion of exact isovists using a simple ray casting approach. Second,we define, create, and use isovist feature cubes for semanticevaluations of floor plans as well as trajectory annotation.

I. INTRODUCTION

The installation of cost-efficient GPS receiver into modernsmartphones has made location-based services (LBS) in out-door scenarios available for millions of people [1]. Use caseslike finding points of interest (POI) in the vicinity, geocachingor navigation systems are amongst the most popular taskswhen using a smartphone. In particular the transformation ofroad networks into graphs using vertices and edges and thesubsequent efficient solving of the shortest path problem hasgot received vast attention in the last years [2].

Calculating and processing routes get more complicatedwhen leaving the quite restricted road network and consideringconstrained free space. Examples of scenarios having a highdegree of freedom of movement are public parks or complexbuildings like hospitals or airports. When driving a car ona highway there are few possibilities to act besides drivingahead or changing the highway at an interchange. In contrast,a pedestrian inside a building can potentially choose to walkin every direction while avoiding obstacles.

Geographic information systems (GIS) are essentially one ofthe foundations for creating location-based services [3]. GISdeal with the acquisition, processing, organization, analysis,and presentation of spatial data. The tasks just stated can gethighly complex based on the underlying use case. There isthe problem of defining the appropriate number and typesof representations necessary for the given use case, and thenot to be underestimated modelling effort to capture thespatial data. Given the example of a common office, it is notdirectly obvious which level of detail is suitable to includeinto the system (just walls, static furniture, mobile items),which resolution is needed (decimeter, centimeter, millimeter),and how to deal with curved surfaces (preferably exact curverepresentation or piecewise linear approximation).

One possibility to represent spatial data such as floor plansare occupancy grids [4]. An occupancy grid is a 2D binarymatrix with 1’s representing cells that contain obstacles and 0’srepresenting free and accessible space. There is a whole rangeof applications where occupancy grids are used for perceptionand navigation, mostly with mobile robots in the plane [5] orunderwater [6]. The main drawback is the fact that occupancygrids are highly erroneous, at least caused by the mappingalgorithms and the sensors prone to noise [7]. Nevertheless,there are huge benefits regarding the performance of certainoperations and the simplicity of their generation. In time ofneed a simple photograph of an analog floor plan may besufficient.

The philosophy of this paper is to incorporate the concept ofisovists for creating new types of indoor LBS [8]. An isovistis basically the volume of space that can be seen from a givenpoint in space [9]. This concept is widely used for decades tostudy the human perception of indoor space. As we focus on2D maps, our understanding of an isovist is the area of a floorplan that is visible from a given point in the plane.

This paper has got two main contributions. First, we pro-pose an approximation of isovists using a simple ray castingapproach, thus defining discrete isovists. Second, we defineand create isovist feature stacks that will be used for semanticevaluations of floor plans as well as trajectory annotation.Basically, we want to identify significant changes of the localenvironment using the isovist feature stacks just mentioned.

There are several applications conceivable such as the an-notation of routes for creating textual representations usable inaudio guidance, the enhancement of map quality by identifyingdoors or other structures that are previously not contained inthe map, or the scheduling and planning of mobile robots,for instance, that perform tasks just at certain spots like largerooms or doors.

The remainder of this paper is structured as follows: InSection II we review related work in the field of isovistanalysis and the relation between occupancy grids and indoorlocation-based services. Section III introduces our conceptof discrete isovists, isovist feature stack, feature maps, andtrajectory annotation. After evaluating our approach in SectionIV we conclude our paper and address envisioned future workin Section V.

Fig. 1. An example of an isovist in an environment with one obstacle (blackrectangle). The black circle indicates the point of view, the gray surface is thearea Ax of the isovist itself, the green lines indicate the real-surface perimeterPx, and the red lines correspond to the occlusivity Qx.

II. RELATED WORK

This section reviews related work regarding isovist analysisand the relation between occupancy grids and indoor LBS.

A. Isovist Analysis

A key research question of behavioral researchers andenvironmental psychologists is the connection between visualproperties of an environment and human’s subjective reactionto it. Probably the first mention of the term isovist has beendone by Tandy in [10], where an isovist got introduced asall the points in space that are visible from a given point ofview. Thus, an isovist can be regarded as a unique fingerprintof a specific spatial configuration of a certain point. It is notunique in the environment, but identical isovists have identicalfeatures. Based on Tandy’s work, Benedikt has developed aformal definition of isovists and introduced a set of measure-ments that enable the numerical analysis of a given spatialenvironment [9]. Based on Benedikt’s definition there are sixisovist features for a 2D environment:

1) Ax: the area of the isovist. The higher the value themore space is visible from the viewpoint.

2) Px: the real-surface perimeter of the isovist. It indicatesthe amount of visible obstacle surface like walls, forexample.

3) Qx: the occlusivity of the isovist. It indicates the lengthof the occluding radial boundary. It can be imagined asa virtual ray passing an obstacle and going through theroom.

4) M2,x: the variance of the isovist’s radius.5) M3,x: the skewness of the isovist’s radius.6) Nx: the circularity of the isovist. This isoperimetric quo-

tient is calculated using Nx = |∂Vx|2/4πAx, whereas|∂Vx| indicates the isovist’s perimeter.

Figure 1 illustrates an isovist with its area (gray surface),real-surface perimeter (green lines), and occlusivity (red lines).

The calculation of exact isovists in a GIS data model is com-plicated. An isovist is a polygon having two types of edges.One type is given by edges lying on top of building geometry

(e.g. walls) and the other type is given by edges crossing freespace. For simplicity, we will discuss the case of a GIS modelconsisting of line segments only. Note that all other entitiesin two-dimensional geometry can be sufficiently representedas line segments. An easy way to obtain an isovist polygonis given by the following procedure creating a list of points:First, a candidate line segment of the building geometry is tobe retrieved. This can be done, for example, by intersecting arandom line through the viewpoint of the isovist with buildinggeometry. The nearest segment intersection between this rayand the GIS model will be a bootstrap segment. Using thissegment, we can follow the right hand rule by choosing theadjacent line segment which forms the smallest angle with thecurrent segment. We do so as long as there is such a segmentand the triangle formed by the line segment and the basepointis empty. In case there is no adjacent geometry to follow, weintersect a ray from the base point through the endpoint ofthe current segment and create a free space segment for ourpolygon. We insert the intersection point to the list of pointsand continue using the right hand rule as above. In case thetriangle is nonempty, we have to find the first line segmentinside it such that the triangle defined by intersecting a raythrough this point with the current segment, the segment startand the base point is empty. We then use this line to createa free space segment from the intersection point to the firstpoint of the previously identified line segment and continuewith a right hand rule polygon traversal. Finally, this defines apolygon in which every line segment of the polygon is eitherlying fully in free space touching building geometry in theend points or lies entirely on top of building geometry. Thispolygon is the isovist.

As evident from the discussion above, there are complicatedcase distinctions and non-trivial GIS queries such as find-ing the nearest intersecting line segment, checking whethera triangle is empty or not, enumerating all segments thatfall inside a triangle, and similar. While these can be spedup by careful spatial indexing such as R* trees [11], theoverall complexity is high. Furthermore, long line segmentscan deteriorate the indexing performance and we might becompelled to subdividing all geometry to line segments that areshort enough for efficient querying. But then, the number ofline segments might become high and the overall complexityof the algorithm increases.

With our idea of discrete isvosists, we gain a high amountof performance for accepting a bounded amount of error dueto discretization. While the computation of the exact isovistis linear in the number of line segments of the isovist and,thus, depends mainly on the map, it can be fastened by spatialindexing. However, spatial indexing works better for smallline segments increasing the complexity of the computation.Additionally, all further computations using the isovist willbe parametrized by the number of edges of the isovist, whichalso increases when subdividing line segments. For the discreteisovist, we first subdivide to a constant length given by therasterization. But additionally, we simplify geometry directlyto pixels such that line intersection can be done using line

painting algorithms such as Bresenham algorithm [12]. Thisleads to a fast approximation of the exact isovist. We followthe same idea of calculating the isovist by turning a scanlinearound the clock from the base point. But this time, we justintersect this line with the building geometry abstracted in theraster map using a variant of line painting. This can be donequickly and with limited error during computation. However,the number of rays to be cast into the environment needs tobe chosen carefully. If the number too high, the computationalcost exceeds the calculation of an exact isovist; if it is chosentoo small, chances are that it will miss important geometry.The same is true for choosing the discretization parameter(e.g., the pixel size). All measures derived from discreteisovists will be limited to the accuracy of representation aspixels.

B. Occupancy Grids and Indoor LBS

As already stated in the introduction, the scientific commu-nity of robotics and automation widely adopts occupancy gridsfor the perception of space and the navigation therein. Besidesthat, the calculation of alternative routes inside buildings is stillan emerging field of research. The authors in [13] define alter-native routes using the topological concept of homotopy [14],[15]. The rather complex test on homotopy has been simplifiedusing occupancy grids as the underlying map representation.Two routes having the same start and end point are calledhomotopic and thus equivalent, if the polygon generated bythe concatenation of the routes has got no obstacles (i.e., blackpixel) inside. Furthermore, the authors proposed algorithms togenerate said alternative routes.

Based on the definition and algorithms proposed in [13]there were continuing works based on alternative routes cal-culated using occupancy grids. The author of [16] created aheatmap showing the frequency of a pixel’s traversal and basedon that defined a congestion probability. The authors of [17]defined archetypal routes, i.e. preferably diverse routes thatgot selected by a fuzzy clustering algorithm based on a set ofsimple to compute features. As mentioned, both works baseon occupancy grids.

III. CONCEPT

Given an occupancy grid in the form of a monochromebitmap, whereas white pixels indicate free and walkable spaceand black pixels indicate obstacles like walls or furniture.Based on this floor plan, we generate a navigation graph thatwill be used in the evaluation, whereas each white pixel isa vertex and each neighboring white pixels create an edge.Horizontal and vertical neighboring pixels create an edgeweight of 1, diagonal neighbors get an edge weight of

√2.

A. Discrete Isovists

Our first contribution is the approximation of an isovist,i.e. the definition of discrete isovists. This paper’s main focusis on simplicity, for which reason we propose a simple raycasting approach. We define a parameter α and accordingly”shoot” 360/α rays radially from a given vantage point. The

Fig. 2. An exemplary exact isovist (left-hand side), a discrete isovist withα = 30 and γ = ∞ (middle), and a discrete isovist with α = 30 and γ = 30(right-hand side).

expansion of a ray proceeds until it hits an obstacle or themap’s boundary. For each of the point’s rays we store theparameter α, the coordinates of the starting and end point aswell as the length.

Besides the number of rays there is another parameter γ, i.e.the maximal length of the rays serving as a length threshold.If γ is set to infinite, then the ray expansion just stops whenhitting an obstacle or the map’s boundary. If γ is set to anatural number, however, the expansion stops not later thanthe threshold. The configuration of this parameter has goteffects on both, the algorithm’s runtime and the results. Figure2 shows an exemplary exact isovist (left-hand side), a discreteisovist with α = 30 and γ = ∞ (middle), and a discreteisovist with α = 30 and γ = 30 (right-hand side).

The actual goal of the approximation is the determinationof the six isovist features rather than the isovist itself. Wedefine the area of the discrete isovist (Ax) to be the areaof the polygon that emerges when connecting the end pointsof the rays. The variance (M2,x) and skewness (M3,x) ofthe isovist is defined as the variance and skewness of thelength of the rays based on the Euclidean norm. The featurecircularity is defined as the squared perimeter of the isovistdivided by 4πAx. The area Ax is already described, we definethe perimeter of the isovist as the perimeter of the polygon thatemerges when connecting the end points of the rays.

The choice of simplicity and the resulting ray castingapproach lead to the fact that the two features real-surfaceperimeter (Px) and occlusivity (Qx) can not properly beapproximated. Thus, we remain with few simple operationsto approximate four of the six isovist features.

One should keep in mind that the discretization using rastermaps introduces systematic errors. For example, every pixelthat contains a marginal amount of building geometry willget occupied, i.e. black. When measuring area or distance bycounting, this should be considered: All measures of free spacewill be smaller than they truly are, as black pixels might notbe fully occupied by geometry.

B. Isovist Feature Stack

After defining discrete isovists and the corresponding fea-tures, we now need to prepare the concept to make it suitablefor floor plan and trajectory analysis. We take a floor plangiven as a bitmap, thus a 2D map, calculate the discrete isovist

Fig. 3. Transformation of an isovist feature stack to a feature map.

features for each walkable pixel, and stack the four measuresover the 2D space in order to get a isovist feature stack. Thestack consisting of the occupancy grid and four isovist featuresfor every x and y cell is visualized in Figure 3, left-hand side.

C. Feature Maps

The first application of the isovist feature stack is theanalysis of the underlying floor plan. For this, we create afeature map by clustering the four features using a clusteringalgorithm like k-means [18], for example. Using this approach,we compress the feature stack into a feature map storing thecluster IDs (see right-hand side of Figure 3). Additionally,this representation can be considered as a dasymetric mapping[19], since it represents data as stepped statistical surfaces. It isnot a choropleth map because the shape of the data polygonsare based on the collected and represented data and not onpredefined areal units.

D. Trajectory Annotation

The second application of the isovist feature stack is theanalysis of routes inside the underlying floor plan. Given a setof routes, each as a vector of x and y coordinates, we selectthe appropriate features from the feature stack. The approachis now similar to the one before, but instead of clustering weinvestigate the local minima and maxima of the resulting fourtime series. For calculating the extrema we need a parameterµ, the size of the sliding window. The implementation ofour algorithm automatically highlights the resulting extremain both, the feature space (time series) and the observationspace (floor plan).

IV. EVALUATION

We base our evaluation on a floor plan of the main buildingof the Technische Universitat Munchen (TUM). This realworld scenario is appropriate because of its complexity dueto numerous entries, rooms, hallways, and a patio. We haveperformed several analysis, below there are exemplary andrepresentative results in form of a route that traverses thebuilding in a large circle (see for example Figure 5b).

A. Error Distribution

We want to examine the difference between the discreteand exact isovist features, i.e. the error of the discretization.Since there are different scales we have to use the normalizedroot-mean-square error (NRMSE). We have calculated the sixexact isovist features using a shape file of the floor plan andperformed a grid tesselation such that one pixel corresponds

TABLE INRMSE OF DISCRETE ISOVIST FEATURES

α γ Ax M2,x M3,x Nx

1 ∞ 0.34 0.29 0.05 0.045 ∞ 0.32 0.29 0.08 0.0820 ∞ 0.25 0.32 0.27 0.2360 ∞ 0.15 0.30 0.37 0.28

1 300 0.34 0.33 0.23 0.135 300 0.25 0.29 0.28 0.2420 300 0.25 0.29 0.28 0.2460 300 0.16 0.32 0.37 0.28

to 0.5m in the real environment. Furthermore, we have calcu-lated the four discrete isovist features using several parameterconfigurations, each indicated in the respective paragraph.

Table I shows the NRMSE of the discretization using fourdifferent values for the ray angle α and two different valuesfor the length threshold γ. To get a better impression, Figure4 shows the resulting features for configuration α = 1 andγ = ∞. The x-axis represents the time, i.e. the progress ofthe mentioned route traversing the building, and the y-axisindicates the corresponding value of the discrete isovist featureat that particular point.

B. Trajectory Annotation

We now perform an analysis of the measures from atime series perspective. The setup is still based on the routetraversing the TUM building in a circle, see for example 5b.For each point of the route we get the corresponding exact anddiscrete isovist features and highlight the local minima andmaxima in both, time series representation and the floorplan.As already stated, there is the parameter µ representing thesize of the sliding window for extrema calculation. For a betterpresentation we fix µ = 300. At first we want to highlight anddiscuss the results for area and variance of exact isovist.

The values regarding the area (Ax) of the exact isovists andthe visualization of the minima and maxima can be seen inFigures 5a and 5b, respectively. The greatest maximum (lastone) is located at the spot where the route traverses the patiohaving a far view, right before it enters the building again.A quite interesting scope is the very stable part between the3rd and 4th maximum, see Figure 5a around the middle andin Figure 5b the long floor going upright. The hallway isvery monotone, that is why we experience constancy in both,feature space and observation space. The two lowest minima(the two last ones) are the spots of the route where a pedestrianwould be cramped.

The values regarding the variance (M2,x) of the exactisovists and again the visualization of the minima and maximacan be seen in Figures 5c and 5d, respectively. A kindobservation is the fact that the maxima correspond exactlyto passed or traversed doors. The highest minimum (4th) canbe regarded as a ”diagonal” door and gets highlighted as well(see Figure 5d, around the middle of the left-handed uprighthallway), although not as a maximum.

(a) Area of isovist Ax (b) Variance of the isovist’s radius M2,x

(c) Skewness of the isovist’s radius M3,x (d) Circularity of the isovist Nx

Fig. 4. Comparison of the exact isovist features (black line) and the discrete isovist features (red line) using parameter configuration α = 1 and γ = ∞.The x-axis represents the time, i.e. the progress of the route traversing the building, and the y-axis indicates the corresponding value of the discrete isovistfeature at that particular point.

After discussing the values of the exact isovist, we nowturn to the discretization. For this, we set γ = ∞ (unrestrictedrays), α = 1 (360/1 = 360 rays) and again µ = 300. SeeFigure 6 showing the area (Ax) and variance (M2,x) of thediscrete isovists in both, the feature space and observationspace. It is easy to see that the time series of the exact anddiscrete isovists have got a very similar process. Accordingly,the positions of the extrema relate strongly to the ones of theexact isovists. Thus, the discussion of the exact values holdshere as well.

V. CONCLUSION

In this paper we proposed to take simple geospatial trajec-tories and floor plans to annotate them by using features thatrepresent the local environment. Isovists are suitable means forthis, as they are used as a numerical representation of spatialimpressions of humans since decades.

We have introduced discrete isovists, i.e. approximatedexact isovists using a simple ray casting approach. Further-more, the resulting discrete isovist features have been usedto create an isovist feature stack which, in turn, has been

utilized to perform a semantic evaluation of routes in an indoornavigation scenario. It is apparent that isovists can be used toannotate routes.

As future work we envision to perform more sophisticatedinformation retrieval on the isovist feature stack, the simpleimplementation of the remaining two isovist features, and theadditional use of circular statistics [20].

ACKNOWLEDGMENT

The authors would like to thank Hao Lyu, who provided theGIS-based exact isovist implementation and related datasets aswell as for his helpful comments on this study.

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(a) Area of isovist (Ax) in feature space (b) Area of isovist (Ax) in observation space

(c) Variance of radius (M2,x) in feature space (d) Variance of radius (M2,x) in observation space

Fig. 6. Local extrema of discrete area and variance of isovist using α = 1 and γ = ∞. Green lines/circles indicate local minima and red lines/circles indicatelocal maxima.

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