Intelligent Land Vehicle Navigation
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Transcript of Intelligent Land Vehicle Navigation
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INTELLIGENT LAND VEHICLE NAVIGATION:
INTEGRATING SPATIAL INFORMATION INTO THE NAVIGATION
SOLUTION
Stephen Scott-Young ([email protected])
Dr Allison Kealy ([email protected])Dr Philip Collier ([email protected])
Department of Geomatics, The University of Melbourne, Vic 3010
Tel. +61 3 9344 6806
Fax. +61 3 9347 2916
Key words: intelligent navigation, integrated systems, GPS, DR
ABSTRACT
Successful intelligent land vehicle navigation systems can only be realised through the
integration of navigation data and spatial information. This is evident in the development
of modern Intelligent Transportation Systems (ITS), where the Global Positioning System(GPS) is used to provide the navigation data, and spatial information contained within an
information database is used to provide location details. With plans already underway for
the development of a Global Navigation Satellite System (GNSS), the next generation ofITS will definitely incorporate satellite positioning technologies.
Unfortunately, the performance of any satellite technology is restricted in areas where sky
visibility is completely or partially obstructed. There is a fundamental requirement to provide a robust navigation system to support future developments of ITS. Potentialsolutions include the development of integrated systems, which combine measurements
from GPS and other complementary sensors, such as dead reckoning (DR), to improve the
continuity of positioning. However, current integration algorithms, such as Kalman
filtering, have difficulty in contending with the high dynamics of land vehicles, andchallenge the navigation capability of these systems within the environment of urban
canyons. Ironically this is perhaps the one environment where the successful application
of satellite technology could most benefit the ITS industry.
This paper discusses the integration of the inherent intelligence of spatial information
contained within a Geographical Information System (GIS) with measurements receivedfrom a navigation system. The spatial information provides additional data that is used to
constrain the navigation solution and provide a more accurate and reliable position
estimate. With this approach, the solution is not dependent on the performance capabilities
of the navigation sensors alone. It enables the use of lower accuracy navigation devices,thereby reducing the cost of navigation systems while still providing a viable solution.
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INTRODUCTION
Intelligent navigation is the process of improving the basic solution obtained from low cost
navigation sensors for land mobile applications. This is achieved through the integration of
measurements provided by the navigation instruments with additional spatial information
contained within a map database. In the majority of current real-time vehicle navigationsystems, a low cost GPS receiver is used to provide information on the vehicles position,
and a Geographic Information System (GIS) is used to provide location details. For land
vehicle navigation applications, GPS only systems are incapable of maintaining continuousnavigation capability in environments where the satellite signals are obstructed (e.g. by
buildings, trees etc). Solutions to this problem commonly involve the integration of GPSwith dead reckoning (DR) sensors. This solution often increases the overall cost of the
navigation system with little improvement in the solution, as DR systems suffer from the
accumulation of errors over time. Additionally, complex Kalman filtering algorithms usedfor a more rigorous integration of GPS and DR measurements are often unable to cope with
the high dynamics of land vehicle navigation.
With the wealth of information contained in a GIS, data can easily be extracted and
integrated into the vehicle navigation solution. In this way, apart from assuming a passive
role of informing users about objects of interest in their surroundings, the information
contained in the database is used as additional measurements within the navigationsolution. This type of integration offers a solution that is capable of improving the
accuracy and performance of low cost, low precision sensors for urban land vehicle
navigation.
DESIGNING A NAVIGATION SYSTEM
The intelligent land vehicle navigation system developed for this research consists of both
hardware and software components. The real-time navigation hardware component
consists of:
a low cost Garmin GPS receiver;
a KVH fibre optic gyro (FOG);
a Pentium 133, 64 megabytes laptop computer;
an odometer.
The software module developed in Smallworld MagikTM
and Microsoft Visual BasicTM
provides a user interface to the navigation software, a means of accessing the GIS database,
as well as enabling intelligent navigation through the integration of measurements from the
GIS with those from the real-time navigation system.
The Hardware Components
The system developed for this project is modular in its design. It therefore enables easy
integration with various types of navigation instruments and techniques. Three modes ofnavigation are tested within this research:
satellite navigation;
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DR navigation;
combined GPS/DR navigation.
The satellite navigation mode relies solely on the GPS receiver. With the recent removal ofselective availability (SA), the position data obtained from the GPS receiver is accurate to
12 meters 95% of the time (Hooper, 2000). The Garmin GPS 45
TM
receiver used cantrack up to eight satellites simultaneously, supports the National Marine ElectronicsAssociation (NMEA) 0183 electrical interface and data protocol standard for
communication between marine instrumentation, and has an RS-232 serial communication
output (Garmin International, 1994). The specific NMEA sentences used by the navigationsystem were the Recommend Minimum Specific GPS/TRANSIT Data (RMC) and Global
Positioning System Fix Data (GGA) sentences.
The DR navigation mode utilises the change in the vehicle direction measurements from aKVH FOG and distance measurements from the vehicles odometer. The FOG has an RS-
232 serial communication output at 9600 baud and is capable of measuring a maximum
rotation rate of 100/second (KVH Industries, Inc., 1999). The FOG allows for input froma vehicles odometer in the form of electrical pulses. Each pulse represents an amount of
wheel rotation predetermined by the vehicle manufacturer. Data received from the
odometer is converted into binary format and included with the information transmitted viathe FOGs RS-232 output. This data is then used to compute distance travelled by the
vehicle.
The accuracy of the DR system is limited predominantly by distance measurement and is
approximately 2% of the distance travelled. Since the DR system contains no means of
absolute positioning, navigation requires the provision of a starting location and direction.
The combined navigation mode integrates both the GPS and DR sensors. In this research,because of the high relative positioning accuracy offered by the DR sensors, the navigation
system relies primarily on DR, resorting to the GPS navigation solution only when thedifference between independently measured GPS and DR positions agree to an expected
level. To define this tolerance, the DR and GPS accuracies were taken into account. Given
that the major error accumulation in DR measurements is from distance measurement andthat the GPS measurement is accurate to 12m 95% of the time, the difference between DR
and GPS position calculations should be within:
(12m + 2% of distance travelled since last GPS measurement used)
A flow diagram of the system hardware and data flow is depicted in Figure 1.
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Unlike the GPS receiver, the FOG does not constitute a low cost instrument. It was used
initially to implement and refine the models for intelligent navigation. However,subsequent testing described in this paper will show that with intelligent navigation, such
high accuracy devices are not required.
The Software Component
Implementation of the intelligent navigation system required a platform to provide a userinterface to the navigation software, to facilitate the data integration between the differenthardware, and to analyse and display spatial data. Smallworld 3
TMGIS was chosen for this
purpose. Smallworlds open architecture and comprehensive spatial analysis functionality
offered significant benefits in developing the software component of this project. Theprogramming language of Smallworld 3 GIS is Magik
TM, an object-oriented programming
language that is also used to implement the majority of the core Smallworld 3 GIS product
itself.
Smallworld 3 GIS includes facilities for integrating applications programmed in languages
other than Magik. This was a particular advantage as it enabled interpretation of the
navigation device outputs to take place in Microsoft Visual BasicTM. While Magik couldhave been used instead, Microsoft Visual Basic contains comprehensive serial
communication libraries that aided development in the communication between Smallworld
3 GIS and the GPS receiver and FOG.
A flow diagram of the data through the navigation system software is shown in Figure 2.
GPS
ReceiverFOG
Odometer
Laptop
MEA sentences
Wheel rotation pulses
DR binary data
Figure 1 - Flow diagram of the system hardware and data flow
Smallworld
GIS
RS-232 connection
DR binary data
Translation of NMEA sentences
into the individual components
of the RMC and GGAnces by Visual Basente sic
Translation of binary data into
the individual DR components
(change in direction and
distance) by Visual Basic
GPS NMEA sentences
RS-232 connection
Satellite navigation data DR navigation data
Figure 2 - Flow diagram of the data through the navigation system software
GPSReceiver
Laptop
Odometer
FOG
Translation of NMEA sentences into the individual
components of the RMC and GGA sentences by Visual Basic
Translation of binary data into the individual DR components (change in direction and distance) by Visual Basic
Smallworld GIS
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The User Interface
The user interface for the intelligent navigation system was designed to minimise the
amount of technical information supplied to the user, with its primary aim being simplicity
of use. The user interface designed is shown in Figure 3.
Position
information
Navigation
device in use
Number of
satellites
visible to
GPS receiver
Navigation options, such as turningintelligent navigation on or off,
automatically centering on the
vehicle location and selection of
navigation mode (i.e., GPS, DR or
both)Figure 3 The navigation system interface
Accessing the Database
Spatial data is a fundamental requirement for intelligent navigation. The road centreline
data for metropolitan Melbourne was stored in the Smallworld 3 GIS database. This data
can then be accessed via Magik, thus providing the essential link between navigationinstrument data and spatial information.
INTELLIGENT NAVIGATION
Four principle rules of intelligent navigation have been identified in this research:
closest road
bearing matching
access only
distance in direction
Closest Road
The first step towards intelligent navigation is to make the assumption that the vehicle is
travelling along a road (which is typically the case). This constraint can be included in the
location solution, thus improving the accuracy of the computed position of the vehicle.
This simple algorithm is effective when the nearest road is in fact the road being travelled.
However, when approaching intersections or when two roads are close to each other, the
Positioninformation
Navigationdevice
Number ofsatellitesvisible to GPS receiver
Navigation options, such as turning intelligent navigation on or off, automatically centering on the vehicle location and selection of navigation mode (i.e., GPS, DR or both)
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nearest road may not be the road being
travelled. In these situations, searching forthe nearest road downgrades the position
solution (Figure 4).
Calculated
position
Actualposition
Actual position
Calculatedposition
(a) (b)
Figure 4 - Correcting to the nearest road: (a)
Navigation without correction. (b) Navigation with
correction.
Measured
position
Additional errors in DR navigation mayarise. One such error occurs as the vehicle
turns a corner. Due to accumulation of
small distance errors, when turning acorner, the nearest road can still be the
previous road of travel (Figure 5).Without the ability to determine absolute
position, further DR navigation becomes
increasingly erroneous.
Bearing Matching
Clearly, as the closest road rule takes into
account only absolute position and not
vehicle bearing, this rule alone is not
sufficient. The second rule, bearingmatching, requires that the nearest road to
which the vehicles position is corrected
must have a similar bearing to the directionof travel. This corrects the problems
previously described. The threshold of
similarity between the vehicles bearingand the bearing of the surrounding roads
may be adjusted to suit the accuracy of the
navigation instruments. However, the
larger the threshold, the more likely roadswill be incorrectly matched as having the
same bearing as that of the vehicle.
(a) (b)
Figure 5 - Correcting to the nearest road with
accumulated distance error : (a) Navigationwithout correction. (b) Navigation with correction.
The significance of this rule must not be overlooked when navigating using DR. Typically,
the largest error source is introduced from distance measurements. As distances are
dependent on wheel rotation, the odometer measurement is affected by tyre condition, pressure variation and vehicle speed (Madhukar et al., 1999). The combination of the
closest road and bearing matching rules adjusts for this error each time the vehicle changes
bearing above the threshold amount. For instance, the distance error, shown in Figure 5,is removed by intelligent navigation. The more often the vehicle turns a corner, the more
frequently accumulated distance error is eliminated.
Using DR as the only source of navigation over long periods of time, the accumulation of
distance error may cause the navigation solution to become invalid. However, provided
that regular change in direction occurs, as is often the case with city driving, accuratenavigation by DR can continue.
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Access Only
Figure 6 shows a case where application of
the closest road and bearing matching rules
incorrectly position the vehicle. Theaccess only rule is designed to identify and
prevent this error from occurring.
Take, for example, a vehicle travelling
along road A in the road layout diagramshown in Figure 7. Assuming the only
route to road C is via road B, logic dictates
that for the vehicle to be travelling alongroad C it must previously have travelled
along road B. By logging previously
travelled roads, the navigation system canprevent the vehicle from being located on
a road that it could not possibly be on.
Distance in direction
This final rule further reduces the
accumulation of distance error bycalculating the distance travelled by the vehicle in the direction of the road rather than the
direction measured by the navigation device. This is particularly important when
navigation instruments of low accuracy are employed. For example, if a vehicle travels1000m along a road of bearing 60 while
measuring the road to have a bearing of
65 (i.e. 5 in error), an error in distance
of 4m will occur (Figure 8). Although thismay seem insignificant, over several
kilometres, or with lower accuracy
navigation instruments, larger errors canaccumulate. This error is avoided by
calculating the distance travelled
independently from the bearing of thevehicle and then applying this distance in
the direction of the road being travelled.
Figure 7 - Road layout scenarioRoad A
Road B
Road C
5
1000m
996m
4m
Figure 6 - Correcting to the nearest road taking
road bearing into account: (a) Navigation without
correction. (b) Navigation with correction.
Calculatedposition
(a)
Actual
position
(b)
1000m
Figure 8 - Distance error propagated from bearing
measurement error.
IMPLEMENTING INTELLIGENT NAVIGATION
The four rules of intelligent navigation were implemented using the Magik programming
language. The fundamental requirement of the algorithm is the ability to search for roads(defined by centrelines in the GIS database) in the vehicles vicinity (as determined by the
navigation instruments). These road centrelines can then be interrogated for information
such as distance to the uncorrected navigation solution and centreline bearing. The
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intelligent navigation rules are then applied to correct the position solution. If more than
one road matches all intelligent navigation constraints, the closest solution is selected.
PERFORMANCE OF THE INTELLIGENT NAVIGATION SYSTEM
The intelligent navigation rules were tested in two different environments, a suburban testcircuit and an urban test circuit. The 5km suburban test environment was used to determine
the performance of intelligent navigation without interference from external factors, such as
satellite signal obstruction. The 3km urban environment was situated in the Melbournecentral business district where GPS satellite visibility is severely restricted and provided
proof of concept that an integrated navigation system with intelligent navigation in anurban environment could provide an accurate, continuous navigation system.
On the suburban circuit, the different navigation systems of satellite and DR were testedindependently. Figures 10 and 12 show the results of applying the four intelligent
navigation constraints on a small part of the test circuit. For comparison, Figures 9 and 11
show the same part of the test circuit being travelled without intelligent navigation. Thesection of circuit shown in these figures is approximately one kilometre in length.
Figure 9 - Satellite navigation
without intelligent navigationFigure 10 - Satellite navigation
with intelligent navigation
Navigation
began here
Navigation
stopped here
Figure 11 - DR navigation
without intelligent navigationFigure 12 - DR navigation with
intelligent navigation
Start point
End point
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It is clear from Figures 9-12 that intelligent navigation is able to provide improved results.Figure 11 shows the accumulation of error in DR navigation. The start and end points of
the navigation were in fact geographically the same. However, over the 1km section of
suburban test circuit shown in Figures 9 to 12, errors of up to approximately 20m can
accumulate in the DR system (Figure 11). This is reduced to less than 8m over the samedistance when intelligent navigation is implemented (Figure 12).
Further tests were conducted using a dual frequency GPS receiver system to provideaccurate kinematic on the fly (KOF) positions for measurement of the true vehicle
trajectory. This test indicated that the mean RMS error between the intelligent navigationsolution and the KOF solution was approximately 12m with a standard deviation of
approximately 9m. Although this is not significantly different when compared with the raw
GPS solution, which also had a mean of approximately 12m and a standard deviation of9m, the advantages of intelligent navigation are apparent in Figure 13.
Severe errors in
GPS positionmeasurements
possibly caused by
multipathing
Satellite
position
DR
position
Figure 13 Navigating the urban environment primarily relying on GPS
Figure 13 depicts the results of navigation in the urban environment where GPS is primarily
relied upon, only supplementing with DR measurements when insufficient satellite
visibility occurs. During this navigation, urban canyoning caused frequent and prolongedperiods of satellite outage up to 70% of the time. Additionally, multipath and deteriorating
satellite geometry often compromised the precision of GPS measurements when signals
were reacquired. These contributed to the subsequent errors in the navigation solution seen
in Figure 13, where the DR system and the intelligent navigation algorithm are unable tocorrect for these errors. In Figure 14 this situation is reversed by using the DR and
intelligent navigation as the primary navigation tools, only including GPS measurements in
the navigation solution when they agree to the DR results to a specified level (as defined in
Severe errors in GPS position measurements possibly caused by multipathing
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the section DESIGNING A NAVIGATION SYSTEM). The intelligent navigation system
was able to detect when GPS measurements were in significant error and enabled 100%continuous navigation in the urban environment.
Satellite
position
DR
position
Figure 14 - Continuous navigation in urban canyons
The most significant impact of intelligent navigation is on the DR solution. While over the
short term the amount of error correction is small, over longer periods of time intelligent
navigation prevents the accumulation of errors to which DR navigation is prone. Thisenables sustained navigation in DR mode without requiring input from absolute positioningdevices. This factor is important for navigation within the urban environment where the
ability to gain regular absolute positions from GPS may not be possible due to obstructions.
ERROR CAPABILITY TESTING
An important aim of implementing intelligent navigation is to reduce the accuracyrequirements of the navigation devices, thereby reducing the cost. Of the equipment
required, only the FOG provides an issue in terms of cost. An alternative to the FOG
would be to use a low cost digital magnetic compass. However, such compasses are
restricted in accuracy by electromagnetic interference generated by the vehicle.
In order to test the ability of the navigation system to cope with lower accuracy bearingmeasurements, an error was added to the FOG. A random error of 30 was introduced to
each measurement, thus allowing for a 60 window of error (Figure 15).
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Figure 16 shows the results of introducing the 60 random error when travelling the samesection of the suburban test circuit as in figures 9 to 12 without intelligent navigation.
Figure 17 shows the result with intelligent navigation.
30 30
60
Figure 15 - 60 window of error
Figure 16 - DR navigation
without intelligent navigationand random error of30
Figure 17 - DR navigation with
intelligent navigation andrandom error of30
Clearly, intelligent navigation was able to compensate for errors up to 30. It is important
to note, however, that all roads in this area intersected at approximately 90. If roads were
to intersect at around 60, bearing errors greater than 30 could render intelligent navigationineffective. However, with a high degree of error, limitations must be expected.
Integration with other navigation devices (such as GPS) would enable errors to be avoided
or corrected.
CONCLUSION
The integration of spatial information with measurements from low cost navigation sensors
has proved highly successful in improving the continuity and accuracy of the navigationsolution in urban environments. The most significant impact of intelligent navigation is on
DR navigation. Without absolute position capabilities, DR navigation is prone to the
accumulation of errors that eventually render the solution meaningless. Intelligentnavigation, however, largely eliminates this accumulation of errors, enabling sustained DR
navigation without requiring input from absolute positioning devices. This factor is
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particularly important for navigation within the urban environment, as the intelligent
navigation system is able to provide 100% continuity of the navigation solution.
Intelligent navigation requires no additional equipment other than that already available in
commercial in-car navigation systems, yet significantly reduces the accuracy
requirements of navigation instruments. Hence lower cost instrumentation can besuccessfully implemented without compromising navigation performance.
REFERENCES
Garmin International, 1994. GPS 45 Personal NavigatorTM
Owners Manual andReference, Garmin International, U.S.A.
Hooper, G., 2000. The End of SA, GIS User, Australia, Aug. Sept. 2000, 41, pp 18-19
KVH Industries, Inc., 1999. KVH ECore 1000 Fibre Optic Gyro Technical Manual, KVH
Industries, Inc., U.S.A.
Madhukar, B. R., Nayak, R. A., Ray, J. K., Shenoy M. R., 1999. GPS-DR Integration
Using Low Cost Sensors. ION GPS 99, Sept. 14-17, Nashville, Tennessee, pp. 537-544
BIOGRAPHICAL NOTES
Stephen Scott-Young is a final year Bachelor of Geomatics/Bachelor of Science (ComputerScience) student at the Department of Geomatics, The University of Melbourne. His
research interests include global positioning, inertial navigation and geographical
information systems and their integration.
Dr Allison Kealy is currently a lecturer in the Department of Geomatics at the University of
Melbourne, specialising in the research areas of GPS, GLONASS and integrated systems.
Allison received her PhD in Geodesy from the University of Newcastle upon Tyne, UK in1996, after which she spent 2 years in industry providing technical support for
GPS/GLONASS manufacturers Ashtech Ltd.
Dr Philip Collier is a Senior Research Fellow in the Department of Geomatics at the
University of Melbourne. His research interests include; GPS deformation monitoring,
dynamic least squares adjustment, and geoid modelling by least squares collocation.