Autonomous vehicle localization method based on an extended Kalman filter and geo-referenced landmarks
DOI:
https://doi.org/10.19053/20278306.v12.n1.2022.14213Palabras clave:
vehículos autónomos, localización de robots, filtros de Kalman, radar láserResumen
Autonomous vehicles are considered a viable technological option to implement first/last mile transportation in the cities of tomorrow with a high population density, and for this reason it is essential that they have a robust localization system for the routes first-mile transport and last-mile transport points, and the route’s planning and navigation. This article presents the implementation of an outdoor parking localization system which uses a map based on geo-referenced landmarks (road marking poles with reflective tape) and an Extended Kalman Filter, fed with both odometry and 3D LiDAR information. The system was evaluated in nine routes with distances between 85 m and 360 m, in which an error was obtained between the ground-truth and the algorithm’s estimated position below 0.3 m and 0.5 m for the position in X and Y coordinates, respectively. The results show that this is a promising method that should be tested in larger settings using both natural and artificial landmarks.
Descargas
Citas
Boston Consulting Group. (2016). Robo-Taxis and the Urban Mobility Revolution.
Brenner, C. (2009). Global localization of vehicles using local pole patterns. In Pattern Recognition: Vol. 5748 LNCS, 61–70. https://doi.org/10.1007/978-3-642-03798-6_7 DOI: https://doi.org/10.1007/978-3-642-03798-6_7
Brenner, C. (2010). Vehicle localization using landmarks obtained by a lidar mobile mapping system. Proceedings of the Photogrammetric Computer Vision and Image Analysis, Vol. 38, XXXVIII, 139–144. http://www.isprs.org/proceedings/xxxviii/part3/a/pdf/zwi/25-XXXVIII-part3A.pdf
Chong, Z. J., Qin, B., Bandyopadhyay, T., Wongpiromsarn, T., Rankin, E. S., Ang, M. H., Frazzoli, E., Rus, D., Hsu, D., & Low, K. H. (2011). Autonomous personal vehicle for the first- and last-mile transportation services. Proceedings of the 2011 IEEE 5th International Conference on Cybernetics and Intelligent Systems, CIS 2011, 253–260. https://doi.org/10.1109/ICCIS.2011.6070337 DOI: https://doi.org/10.1109/ICCIS.2011.6070337
Cui, Y., & Ge, S. S. (2003). Autonomous Vehicle Positioning With GPS in. 19 (1), 15–25. DOI: https://doi.org/10.1109/TRA.2002.807557
Founoun, A., & Hayar, A. (2018). Evaluation of the concept of the smart city through local regulation and the importance of local initiative. 2018 IEEE International Smart Cities Conference, ISC2 2018. https://doi.org/10.1109/ISC2.2018.8656933 DOI: https://doi.org/10.1109/ISC2.2018.8656933
Gallego, G., Cuevas, C., Mohedano, R., & García, N. (2013). On the mahalanobis distance classification criterion for multidimensional normal distributions. IEEE Transactions on Signal Processing, 61(17), 4387–4396. https://doi.org/10.1109/TSP.2013.2269047 DOI: https://doi.org/10.1109/TSP.2013.2269047
Hartmannsgruber, A., Seitz, J., Schreier, M., Strauss, M., Balbierer, N., & Hohm, A. (2019). CUbE: A Research Platform for Shared Mobility and Autonomous Driving in Urban Environments. 2019 IEEE Intelligent Vehicles Symposium (IV), Iv, 2315–2322. https://doi.org/10.1109/ivs.2019.8813902 DOI: https://doi.org/10.1109/IVS.2019.8813902
Hata, A., Osorio, F., & Wolf, D. (2014). Robust curb detection and vehicle localization in urban environments. IEEE Intelligent Vehicles Symposium, Proceedings, Iv, 1257–1262. https://doi.org/10.1109/IVS.2014.6856405 DOI: https://doi.org/10.1109/IVS.2014.6856405
Hata, A., & Wolf, D. (2014). Road marking detection using LIDAR reflective intensity data and its application to vehicle localization. 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), 584–589. https://doi.org/10.1109/ITSC.2014.6957753 DOI: https://doi.org/10.1109/ITSC.2014.6957753
Kos, T., Markezic, I., & Pokrajcic, J. (2010). Effects of multipath reception on GPS positioning performance. Elmar, 2010 Proceedings, September, 15–17.
Kuutti, S., Fallah, S., Katsaros, K., Dianati, M., Mccullough, F., & Mouzakitis, A. (2018). A Survey of the State-of-the-Art Localization Techniques and Their Potentials for Autonomous Vehicle Applications. IEEE Internet of Things Journal, 5(2), 829–846. https://doi.org/10.1109/JIOT.2018.2812300 DOI: https://doi.org/10.1109/JIOT.2018.2812300
Li, L., Yang, M., Weng, L., & Wang, C. (2021). Robust Localization for Intelligent Vehicles Based on Pole-Like Features Using the Point Cloud. IEEE Transactions on Automation Science and Engineering, 1–14. https://doi.org/10.1109/TASE.2020.3048333 DOI: https://doi.org/10.1109/TASE.2020.3048333
Lu, F., Chen, G., Dong, J., Yuan, X., Gu, S., & Knoll, A. (2020). Pole-based Localization for Autonomous Vehicles in Urban Scenarios Using Local Grid Map-based Method. ICARM 2020 - 2020 5th IEEE International Conference on Advanced Robotics and Mechatronics, 640–645. https://doi.org/10.1109/ICARM49381.2020.9195330 DOI: https://doi.org/10.1109/ICARM49381.2020.9195330
Mahalanobis, P. C. (1936). On the Generalized Distance in Statistics. Proceedings of National Institute of Sciences, 2 (1), 49–55.
Otsu, N. (1979). A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics, C (1), 62–66. https://doi.org/10.1109/TSMC.1979.4310076 DOI: https://doi.org/10.1109/TSMC.1979.4310076
Payá, L., Gil, A., & Reinoso, O. (2017). A State-of-the-Art Review on Mapping and Localization of Mobile Robots using Omnidirectional Vision Sensors. Downloads. Hindawi. Com, 2017. https://doi.org/10.1155/2017/3497650 DOI: https://doi.org/10.1155/2017/3497650
Qu, X., Soheilian, B., & Paparoditis, N. (2018). Landmark based localization in urban environment. ISPRS Journal of Photogrammetry and Remote Sensing, 140, 90–103. https://doi.org/10.1016/j.isprsjprs.2017.09.010 DOI: https://doi.org/10.1016/j.isprsjprs.2017.09.010
Schaefer, A., Büscher, D., Vertens, J., Luft, L., & Burgard, W. (2019). Long-Term Urban Vehicle Localization Using Pole Landmarks Extracted from 3-D Lidar Scans. DOI: https://doi.org/10.1109/ECMR.2019.8870928
Schlichting, A., & Brenner, C. (2014). Localization using automotive laser scanners and local pattern matching. IEEE Intelligent Vehicles Symposium, Proceedings, Iv, 414–419. https://doi.org/10.1109/IVS.2014.6856460 DOI: https://doi.org/10.1109/IVS.2014.6856460
Shen, Y., Zhang, H., & Zhao, J. (2018). Integrating shared autonomous vehicle in public transportation system: A supply-side simulation of the first-mile service in Singapore. Transportation Research Part A: Policy and Practice, 113, 125–136. https://doi.org/10.1016/j.tra.2018.04.004 DOI: https://doi.org/10.1016/j.tra.2018.04.004
Soheilian, B., Qu, X., & Bredif, M. (2016). Landmark based localization: LBA refinement using MCMC-optimized projections of RJMCMC-extracted road marks. IEEE Intelligent Vehicles Symposium, Proceedings, 2016-Augus, 940–947. https://doi.org/10.1109/IVS.2016.7535501 DOI: https://doi.org/10.1109/IVS.2016.7535501
Thrun, S., Burgard, W., & Fox, D. (2006). Probabilistic robotics. https://doi.org/10.1145/504729.504754 DOI: https://doi.org/10.1145/504729.504754
United Nations. (2019). World Urbanization Prospects: The 2018 Revision. https://doi.org/10.4054/demres.2005.12.9 DOI: https://doi.org/10.4054/DemRes.2005.12.9
Vernier, M., Redmill, K., Ozguner, U., Kurt, A., & Guvenc, B. A. (2016). OSU SMOOTH in a Smart City. 2016 1st International Workshop on Science of Smart City Operations and Platforms Engineering (SCOPE) in Partnership with Global City Teams Challenge (GCTC), SCOPE - GCTC 2016, 1–6. https://doi.org/10.1109/SCOPE.2016.7515057 DOI: https://doi.org/10.1109/SCOPE.2016.7515057
Vivacqua, R. P. D., Bertozzi, M., Cerri, P., Martins, F. N., & Vassallo, R. F. (2018). Self-Localization Based on Visual Lane Marking Maps: An Accurate Low-Cost Approach for Autonomous Driving. IEEE Transactions on Intelligent Transportation Systems, 19 (2), 582–597. https://doi.org/10.1109/TITS.2017.2752461 DOI: https://doi.org/10.1109/TITS.2017.2752461
Wang, L., Zhang, Y., & Wang, J. (2017). Map-Based Localization Method for Autonomous Vehicles Using 3D-LIDAR. IFAC-PapersOnLine, 50 (1), 276–281. https://doi.org/10.1016/j.ifacol.2017.08.046 DOI: https://doi.org/10.1016/j.ifacol.2017.08.046
Weng, L., Yang, M., Guo, L., Wang, B., & Wang, C. (2018). Pole-based real-time localization for autonomous driving in congested urban scenarios. 2018 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2018, 96–101. https://doi.org/10.1109/RCAR.2018.8621688 DOI: https://doi.org/10.1109/RCAR.2018.8621688
Zhou, W., Worrall, S., Zyner, A., & Nebot, E. (2018). Automated Process for Incorporating Drivable Path into Real-time Semantic Segmentation. 6039–6044. DOI: https://doi.org/10.1109/ICRA.2018.8460486
Descargas
Publicado
Cómo citar
Número
Sección
Licencia
Los artículos aquí publicados están protegidos bajo una licencia Licencia Creative Commons Atribución 4.0 Internacional. El contenido de los artículos es responsabilidad de cada autor y no compromete, de ninguna manera, a la revista o a la institución. Se permite la divulgación y reproducción de títulos, resúmenes y contenido total, con fines académicos, científicos, culturales y/o comerciales, siempre y cuando se cite la respectiva fuente.