Evaluating tram positioning accuracy on curves based on map data and segmented images

   An approach is proposed for evaluating the accuracy of navigation systems using data from technical vision sensors and a digital map. The digital map is defined as arch-linear splines approximating the centerline of the railway track. This approach does not rely on satellite navigation data and is relevant for assessing the quality of navigation solutions for mobile transport vehicles operating in urban environments. The proposed approach is based on comparing segmented images containing railway tracks with digital map data. The study examines two comparison methods: the first based on comparing areas using the IoU metric, and the second based on comparing lines and calculating residuals between them. In the first method, the arch-linear spline of the route is projected onto the image frame, creating a road area based on navigation system readings and digital map data. In the second method, the centerline is extracted from the railway track area in the segmented image and compared with the route spline. Since the residuals generated in both cases are nonlinear, the evaluation of navigation system errors is performed using a particle filter, where each particle defines the coordinates and orientation of the “probable” location of the tram. The tram location and orientation are estimated based on the weighted summation of particles, with higher weights assigned to particles that better align measured data with synthesized areas or lines. The proposed methodology was tested on simulated and real data collected from tram routes in Saint Petersburg. Experiments demonstrated that the first method provides higher accuracy compared to the second, attributable to the need for post-processing segmented image data to extract the railway track centerline, which results in a loss of useful information. The study established a relationship between the accuracy of navigation parameter determination and the road curvature radius, showing a decrease in accuracy on curves with larger radii. The approach applicability for assessing navigation errors and its robustness to varying weather conditions and road surface quality were experimentally confirmed. The proposed approach stands out from known methods due to its simplicity and data accessibility. Compared to methods based on lidar data, it does not require expensive sensors or the labor-intensive process of aligning lidar point clouds with high-precision maps. Unlike methods using technical vision, it eliminates the need for creating landmark maps, developing complex identification procedures or matching processes.

1. Lobanov M.G., Sholomov D.L. Application of shared backbone DNNs in ADAS perception systems. Proceedings of SPIE, 2021, vol. 11605, pp. 1160525. doi: 10.1117/12.2586932

2. Guzhva N.S., Prun V.E., Postnikov V.V., Lobanov M.G., Sadekov R.N., Sholomov D.L. Using 3D object detection DNN in an autonomous tram to predict the behaviour of vehicles in the road scene. Proc. of the 29^th Saint Petersburg International Conference on Integrated Navigation Systems (ICINS), 2022, pp. 1–6. doi: 10.23919/ICINS51784.2022.9815388

3. Dikeman M., Burns C. Trust in autonomous vehicles: The case of Tesla Autopilot and Summon. IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017, pp. 1093–1098. doi: 10.1109/smc.2017.8122757

4. Kukkala V.K., Tunnell J., Pasricha S., Bradley T. Advanced Driver-Assistance Systems: a path toward autonomous vehicles. IEEE Consumer Electronics Magazine, 2018, vol. 7, no. 5, pp. 18–25. doi: 10.1109/mce.2018.2828440

5. Song J.H., Jee G.I. Performance enhancement of land vehicle positioning using multiple GPS receivers in an urban area. Sensors, 2016, vol. 16, no. 10, pp. 1688. doi: 10.3390/s16101688

6. Jimenez F., Monzon S., Naranjo J.E. Definition of an enhanced map-matching algorithm for urban environments with poor GNSS signal quality. Sensors, 2016, vol. 16, no. 2, pp. 193. doi: 10.3390/s16020193

7. Sholokhov A.V., Berkovich S.B., Kotov N.I., Sadekov R.N. Formation of the trajectory of the correlation-extreme navigation system according to the criterion of minimum coordinate errors. Proc. of the 25^th St. Petersburg International Conference on Integrated Navigation Systems, 2018, pp. 175–177. (in Russian)

8. Salleh D.N.S.D.A., Seignez E. Longitudinal error improvement by visual odometry trajectory trail and road segment matching. IET Intelligent Transport Systems, 2019, vol. 13, no. 2, pp. 313–322. doi: 10.1049/iet-its.2018.5272

9. Alonso I.P., Llorca D.F., Gavilan M., Pardo S.A., García-Garrido M.A., Vlacic L., Sotelo M.A. Accurate global localization using visual odometry and digital maps on urban environments. IEEE Transactions on Intelligent Transportation Systems, 2012, vol. 13, no. 4, pp. 1535–1545. doi: 10.1109/TITS.2012.2193569

10. Schaefer A., Büscher D., Vertens J., Luft L., Burgard W. Long-term urban vehicle localization using pole landmarks extracted from 3-D lidar scans. Proc. of the European Conference on Mobile Robots (ECMR). 2019, pp. 1–7. doi: 10.1109/ecmr.2019.8870928

11. Bikmaev R.R., Polukarov A.A., Sadekov R.N. Position location of a land vehicle using a single-camera and road signs with geodetic coupling. Proc. of the 27^th St. Petersburg International Conference on Integrated Navigation Systems, 2020, pp. 341–345. (in Russian)

12. Wang L., Zhang Y., Wang J. Map-based localization method for autonomous vehicles using 3D-LIDAR. IFAC-PapersOnline, 2017, vol. 50, no. 1, pp. 276–281. doi: 10.1016/j.ifacol.2017.08.046

13. Wang C., Huang H., Ji Y., Wang B., Yang M. Vehicle localization at an Intersection using a traffic light map. IEEE Transactions on intelligent transportation systems, 2019, vol. 20, no. 4, pp. 1432–1441. doi: 10.1109/TITS.2018.2851788

14. Murali V., Chiu H.P., Samarasekera S., Kumar R.T. Utilizing semantic visual landmarks for precise vehicle navigation. Proc. of the IEEE 20^th International Conference on Intelligent Transportation Systems (ITSC), 2017, pp. 1–8. doi: 10.1109/ITSC.2017.8317859

15. Qu X., Soheilian B., Paparoditis N. Landmark based localization in urban environment. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, vol. 140, pp. 90–103. doi: 10.1016/j.isprsjprs.2017.09.010