ROAD SIGN SAFETY IDENTIFICATION THROUGH THE USE OF A MOBILE SURVEY SYSTEM

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1 ROAD SIGN SAFETY IDENTIFICATION THROUGH THE USE OF A MOBILE SURVEY SYSTEM S. Foy a, C. Deegan a, C. Mulvhll a, C. Ftzgerald c, C. Markham b, S. McLoughln a a Insttute of Technology Blanchardstown, ITB, Dubln 15, Ireland - stephen.foy@tb.e b Dept. of Computer Scence, Natonal Unversty of Ireland Maynooth, Kldare, Ireland c Natonal Roads Authorty of Ireland, Kldress House, Pembroke Row, Dubln 2, Ireland KEY WORDS: GIS, Moble Mappng, Road survey, Inertal Navgaton wth GPS, Sensor fuson, Road safety. ABSTRACT: Technques are descrbed n the followng paper to ncorporate a GPS wth an IMU sensor to produce more accurate postons and atttude dynamcs for geo-reference mages of roadways. An Extended Kalman Flter (EKF) provdes the possblty to ntegrate values from the two sources whlst mnmzng errors to provde an accurate trajectory of the vehcle. Prelmnary tests have shown that the use of GPS aded INS approaches n the moble mappng system can provde hgh bandwdths for the vehcles knematcs and facltates the generaton of three dmensonal topographc poston. The results demonstrate that low cost MEMS IMU/GPS s a feasble soluton. 1.1 Background 1. INTRODUCTION Rased Pavement Markers (RPMs, Cats eyes), traffc sgns and road markngs have retro-reflectve propertes on the roads. Road pant and traffc sgns deprecate perodcally, whereas RPMs tend to fal wthout havng a fadng perod. Malfunctonng RPMs are one of many factors that contrbute to road traffc accdents (Berg, 2006). Unfortunately, detecton and mappng of broken or damaged RPMs s dffcult on busy motorways. In collaboraton wth the Natonal Road Authorty of Ireland (NRA), research at the Insttute of Technology Blanchardstown, Dubln has resulted n the development of a moble mappng system that s capable of automated dentfcaton of RPMs, traffc sgns and road markngs, see Fgure 1, (McLoughln, 2005). obtaned through sequental dfferencng of GPS measurements (Mulvhll, 2006). The system desgn s low cost, completely portable and can be secured onto any vehcle wth a set of roof bars. The vehcle headlghts cause retro-reflecton on road features. The cameras then record these hghlghted features as stereo mage pars. A standard survey of a road s performed durng perods of low ambent lghtng. The retro-reflectve features are extracted n stereo mage pars by performng mage analyss routnes. These nclude technques such as mage thresholdng, morphologcal analyss, photometrc analyss and connected component labellng. (McLoughln, 2006). Correspondng features are dentfed n the stereo pars usng local correlaton based technques. When correspondng feature ponts are found, trangulaton provdes the Eucldean coordnates n the camera frame. When detectng RPMs, the nter-spatal dstances between studs was used to nfer the number of defectve or mssng studs that lne on the ground plane. Tests have acheved 100% automated detecton of defectve RPMs and a detecton of 94% of road sgns. 1.2 Problem outlne Fgure 1. Road sgn detected on road secton A mappng system s made up of two man features, mappng sensors and postonng sensors. The ntal approach taken for developng the system was to use stereovson wth GPS technologes for road feature detecton and mappng. The postonng component assumed planar moton wth 3 degrees of freedom. The sngle antenna GPS provded all postonng parameters e. Eastng, Northngs and headng. The headng was The earler versons of ths moble mappng system had moderate accuracy and precson. Unfortunately both, atttude and postonng power were lost due to the nablty to accurately calculate them from the GPS measurements exclusvely. The sngle antenna GPS had shortcomngs due to two man problems; sgnal obstructon and ts low refresh rates (Konosh, 2000). Ths effected the computed vehcles-camera absolute geographcal locaton and trajectory. The postonng system nadequaces lead to the development of an mproved vehcle postonng system as descrbed n ths paper. The alternatve soluton was to ncorporate a strap-down Inertal Measurement Unt (IMU) wth a GPS recever to obtan the vehcles atttude and velocty at hgh refresh rate. Ths procedure has been performed many tmes before and there s a large database of knowledge on ths topc (Dorobantu, 1999). IMU obtans measurements for the rate of turn usng a gyroscope and acceleraton usng an accelerometer. These

2 measurements need to be ntegrated over tme to obtan orentaton changes and velocty measurements. The IMU components produce small measurement errors that accumulate over tme and cause drft errors. As a result the sensor s accurate over short tme ntervals but needs to be combned wth other devces to obtan stablty over long measurement perods. The IMU sensors can also be combned wth a magnetometer that uses the magnetc north as a reference to stop orentaton errors. The IMU that was used for ths project was a low cost MEM IMU from XSENS. The nertal unt has an addtonal 3 axs magnetometer wth an on board dgtal sgnal processng unt. Ths processng unt contans a closely coupled KF-based algorthm to provde stable atttude and headng reference (AHRS) data, along wth calbrated acceleraton, angular rate and magnetc feld data. The outlne of the paper s as follows. Secton 2 gves a bref overvew of the entre data-capturng system and data-capturng algorthm, wth partcular emphass on the postonng system. Secton 3 gves an overvew of the nertal equatons and flterng procedures. The fnal sectons gve an ndcaton of the prelmnary results and conclusons. System Hardware 2. DATA ACQUISITION SYSTEM The data acquston system s located n a vehcle roof box consstng of two cameras, a Global Postonng System (GPS), an Inertal Measurement Unt (IMU) and an nterfacng PC (Fgure 1). Three-dmensonal observatons of the roadways are obtaned wth two vehcle-mounted cameras (CMOS IEEE1394). A Garmn GPS and a XSENS IMU sensor obtan the vehcle knematcs. The nterfacng PC s a Sony portable laptop. Ths nterfacng PC s controlled across a wreless network wth another n-car laptop. The two man parts of the GPS/IMU system are: An acquston system that synchronses all the devces and stores the data for post-processng. A navgaton processng system that ntegrates the GPS and INS data to fnd the coordnates and atttude of the moble mappng vehcle over a surveyed road. 3. STRAPDOWN NAVIGATION MECHANISM The Xsens IMU sensor conssts of three orthogonal accelerometers, gyroscopes and magnetometers. Table 1 below gves a reference of the performance specfcatons of the IMU (Xsens, 2006). The IMU can provde computed atttude data along wth calbrated sensor knematcs data. The followng secton wll show nertal navgaton equatons. These equatons are based on Newton s laws of moton, whch can calculate the vehcles acceleraton wth the nertal data. The acceleratons can be ntegrated over tme for velocty and postons measurements. A sensor fuson algorthm uses an Extended Kalman Flter to compensate for errors n the nertal calculatons wth GPS data. Performance Data Values Orentatons Dynamc Range 3D Angular Resoluton 0.05 RMS Statc Accuracy <1.0 Accuracy (headng) 2 RMS Calbrated -Accelerometer Full scale: 17m/s/s(1.7g) Nose densty (unts Hz) Rate gyro Full scale 17 deg/s Nose densty (unts Hz) 0.1 Table 1. XSENS MTI specfcaton 3.1 Atttude and Navgaton Equatons Fgure 2. Data acquston system Synchronsaton of the sensors s an mportant ssue. The cameras capturng frequency s trggered usng hardware around the GPS 1 Hz pulses whle the GPS and IMU synchronsaton s software based. The XSENS sensor can gve calbrated data at a selectable 25Hz to 120Hz. The synchronsaton of the devces s done usng a sequence counter varable and ths value s shared through nter-process communcaton. The sequence counter s appended to both GPS and IMU data beng logged. Assumng that IMU and GPS nternal samplng frequences are stable and all the data s logged the maxmum tmng offset error s <(GPS sample frequency/imu sample frequency). The Earth Centred Earth Fxed (ECEF) coordnates are used as a common reference frame between the GPS and IMU. GPS postons and veloctes are n the ECEF frame. To track a poston the atttude and dstance travelled must be known. The atttude projectons needed are from the frame to the ECEF and are calculated from two rotatons. The frst rotaton, navgaton C (Equaton 1), conssts of the IMU sensor data n navgaton Quaternon form and the second rotaton, C ECEF (Equaton 2), uses the current tangent plane coordnates. Combnng these ECEF drecton cosne equatons gves C (Equaton 3), the rotaton matrx for the common frame (Ttterton, 2004). C navgaton a b c d 2bc ad 2bd ac bc ad a b c d 2cd ab bd ac 2cd ab a b c d 2 (1) where a, b, c, d= Quaternon unts

3 where Navgaton C ECEF sn cos sn sn cos sn cos 0 cos cos cos cos sn = lattude = longtude ECEF ECEF navgaton C Cnavgaton. C (2) (3) error form s desred for the Kalman flter algorthm. Usng a frst order Taylor seres expanson, we can make an approxmaton for the state transton matrx (Rodgers, 2003). There are sx observatons used to update the Kalman flter; three-dmensonal poston n the ECEF frame and threedmensonal velocty also n the ECEF frame. The poston observatons are the relatve dfferences between the INS and GPS postons (Equaton 7). The velocty errors are calculated from the dfference between the INS and GPS calculated veloctes (Equaton 8). The velocty dynamcs (Equaton 4) use the acceleraton forces n the /sensor frame from the accelerometer, subtracts the centrpetal acceleratons en V f, and and gravtaton forces. The Cosne matrx C ECEF s used n the projecton of the gravty vector to the frame. The followng velocty equaton does not take nto account Corols acceleratons, as the effects of the earth s rotatons are small over small dstances and short tmes ntervals. where frame v f en V C ECEF. g (4) V = prevous velocty vector en = angular veloctes of frame g = gravty vector f acceleratons of frame C ECEF = cosne matrx, ECEF frame to The velocty dynamcs are n the frame after applyng Equaton 4. Ths velocty must be transformed to the ECEF navgaton frame (Equaton 5) and then ntegrated over tme (Equaton 6) to fnd the poston vector R. where rimu rgps r n (7) v IMU vgps v n (8) r = poston vector r = poston error v = velocty vector v = velocty error n = whte nose 3.3 Expermental evaluaton The ntal testng of the GPS aded INS algorthm was done usng smulated data. The prelmnary commssonng test for accessng the qualty of the postonng system was conducted n a road envronment Test descrptons: Feld tests wth the data acquston hardware were carred out on a 22-klometre stretch of M4 motorway, located west of Dubln, Ireland. The selected secton of motorway had the sutable test case scenaro, straght and curved sectons wth smooth changes n alttudes as seen n Fgure 3. Both sdes of the motorway were traversed usng a large ntersecton roundabout to turn. The test run was conducted at motorway speeds of 120km/h wth average speed for test beng 62 km/h. When observng the fgures t s worth takng note that drvng s on the left sde n Ireland. ECEF V ECEF C. V (5) t (6) R( t) V. dt R(0) Kalman flterng The EKF was used for the correcton of the poston and velocty dynamcs of the INS sensor wth GPS data. Further readng on the Kalman flterng can be found n (Welch, 2004). The Kalman state vector contans absolute poston, velocty, atttude and gravty calculatons. The state vector conssts of the postons XYZ n the ECEF frame, velocty NED n ECEF frame, atttude n Quaternon form and gravty vector bas. Due to the non-lnear nature of the navgaton formulas, a Lnearzed Fgure 3. Map of test cases The test conssted of usng the GPS and IMU mounted on the roof of a vehcle. The prmary GPS was a Garmn street plot III+ and a second GPS sensor was also used (GARMIN Etrex

4 wth WAAS/EGNOS enabled). Only one set of GPS data was used for drect ntegraton of GPS/IMU whle the second was an addtonal accuracy reference. Data was logged at 1HZ from both GPS sensors and 25HZ from the IMU Evaluaton of data loggng: The use of software events allowed for more accurate data logs and hgher synchronsed data. Ths s advantageous over software pollng, as prorty of the pollng thread cannot always be guaranteed on MS Wndows envronments. In the statc envronment, the logged data reported mnor omssons but on feld-tests, logs had omssons caused by vbraton and shock to the nterfacng PC and the other devces. Table 2 gves an average of logged data whle conductng ths feld run aganst a prevous verson of the capture software that used hardware pollng. Settng Pollng GPS 1Hz Events GPS 1Hz Pollng IMU 25Hz Events IMU 25Hz % Data Captured >91.7 >98.99 on a number of factors such as dluton of precson and sgnal qualty. The mean poston errors were meters wth the prmary GPS and the second GPS had a mean error of 3.04 meter. The qualty of trajectory dfferences can be easly observed n the fgures that follow. 3.4 Results after Kalman Flterng The data was frst fltered usng the prmary GPS to ad the nertal data and the second GPS was used as a reference for the qualty of the flter algorthm. The procedure was then changed wth the second GPS beng used n the flter. The standard devatons for KF updates were obtaned through emprcal analyss and categorzed for dfferent scenaros. The flter updates were carred out wth GPS observaton <5 meters wth the prmary GPS and <4 meters wth the second GPS devce. Fgure 5 gves an observaton of the fltered 3 axes postons n the ECEF frame. Table 2. GPS/IMU logs over 20 mnute averages Types of logged data: The data recorded from the IMU conssted of the two log fles contanng, orentaton, accelerometer and gyroscope data for the 3 axes of the vehcle. Fgure 4 shows orentaton data for the test run wth slght adjustments n the yaw angle for North turn. Xsens, the manufactures of the IMU, supply magnetc feld mappng software. The mappng vehcle contans ferromagnetc materals, whch can cause dsturbances n the IMU magnetometers. The calbraton software results gve a calbraton summary wth an advanced qualty ndcator and an estmated accuracy of orentaton. On ths test run the reportng calbraton accuracy was <1.2 degrees for orentaton. Fgure 5. KM flter postons n the ECEF Frame A secton of the road had a brdge that caused sgnal dsruptons. As the errors were large n the GPS, the flter dd not use updates and so used only nertal data to brdge the errors n the GPS. Over 154 meters of sgnal brdgng occurred wth a 15.5 meter error dstrbuted exponentally. Fgure 4. AHRS Orentaton over test The man nformaton obtaned from the GPS devces was postons, veloctes and a measurement of the poston accuracy. Data logged off the GPS devces were n smple text output protocol. The GPS observaton qualty was based on Horzontal Poston Error (HPE), whch s a calculaton based Fgure 6. Prmary GPS wth satellte error (Road Secton B)

5 The prmary GPS exhbted poor postonng whle travellng at slow speeds. These large poston errors caused a rough roundabout trajectory as seen n Fgure 7. In Fgure 8 a more accurate estmaton can be seen by the system when usng the second GPS for poston updates as the flter has a large dependence on the GPS observatons. Fgure 9. Secton B EKF updates from second GPS recever 4. FUTHURE WORK AND CONCLUSION Fgure 7. EKF wth updates from prmary GPS Durng ntal commssonng the IMU capture was at a mere 25 Hz. Increasng ths up to ts maxmum of 120 Hz should yeld more accurate results. Wthout addtonal enhancements the system s hghly dependent on the GPS observatons as seen n the results. The Kalman covarance matrxes are set to veloctes and postons that hghly thrust the GPS estmates. The prelmnary results conclude that the nertal data wth the GPS sensor can provde the hgher frequency of poston along wth orentaton data. Future work wll see more extensve, statstcal analyss of the errors, wth postons beng modelled n the local grd coordnate system rather that the ECEF frame. Nonholonomc constrants and the subtracton of Corols terms can also be appled to the navgaton equatons to help n the reducton of error. Ths paper has presented an overvew of a currently low cost IMU combned wth low cost GPS. Ths smple and effectve navgaton soluton provdes the assessment of the knematcs and postonng of the vehcle, whch s needed by the prevously bult computer vson applcaton. Fgure 8. EKF wth second GPS Recever The best trajectory can be seen at sectons B and C on the map. The steady smooth change n turn and straght produced accurate observatons. Fgure 8 shows fltered data wth a standard devaton of 0.2 meters for the covarance matrx, R, on both sdes of the motorway. 5. REFERENCES Berg, H-Y., Reducng crashes and njures among young drvers: what knd of preventon should we be focusng on?. BMJ Publshng Group Ltd, 12(1), pp Dorobantu, R., Feld Evaluaton of a Low-Cost Strapdown IMU by means GPS, Ortung und Navgaton, DGON, Bonn. Kngston, D., Real-Tme Atttude and Poston Estmaton for Small UAVs Usng Low-Cost Sensors, AIAA 3rd Unmanned Unlmted Systems Conference. Konosh,Y., Development of a smulaton system to estmate avalable area of GPS and pseudolte. Proceedngs of the 22nd Asan Conference on Remote Sensng, pp McLoughln, S., Classfcaton of road sgn type usng moble stereovson, Proceedngs of SPIE Opto-Ireland, Dubln.

6 McLoughln, S., C.E. Moble Mappng for the Automated Analyss of Road Sgnage and Delneaton, In Proceedngs of the 13th World Congress on Intellgent Transportaton Systems, London. Mulvhll, C, Moble stereo vson for the dentfcaton of defectve road studs, Irsh Machne Vson and Image Processng, Dubln. Mulvhll, C., Moble Stereo Vson for the Mappng of Defectve Road Studs. In Proceedngs of IEEE Advances n Cybernetc Systems (AICS), Sheffeld, England. Rodgers, R.M., Appled Mathematcs n Integrated Navgaton Systems. Amercan Insttute of Aeronautcs and Astronautcs, Vrgna, pp Ttterton, D., and Weston, J., Strapdown Inertal Navgaton Technology. 2nd Edton, Insttute of Electrcal Engneers, pp Welch, G.,. and Bshop, G., An Introducton to the Kalman Flter. Techncal Report TR , Unversty of North Carolna, Department of Computer Scence. Xsens, MTI specfcatons document, Xsens Technologes, Enschede, Netherlands. (accessed 10 Jan. 2006) 6. ACKNOWLEDGEMENTS The authors would lke to thank the Tramore House Regonal Desgn Offce (THRDO), the Natonal Roads Authorty of Ireland (NRA) and the Councl of Drectors of the Insttutes of Technology for ther contnued support durng the research.