intelligent land vehicle navigation

8/7/2019 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.

intelligent land vehicle navigation

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