Autonomous vehicle localization method based on an extended Kalman filter and geo-referenced landmarks

Autonomous vehicle localization method based on an extended Kalman filter and geo-referenced landmarks

Contenido principal del artículo

Resumen

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.

Palabras clave:

Descargas

Los datos de descargas todavía no están disponibles.

Detalles del artículo

Biografía del autor/a (VER)

Breyner Posso-Bautista, Universidad del Valle, Cali

Ingeniero Electrónico, Estudiante de Doctorado en Ingeniería

Eval Bladimir Bacca-Cortés, Universidad del Valle, Cali

Ingeniero Electrónico, Doctor en Tecnología

Eduardo Caicedo-Bravo, Universidad del Valle, Cali

Ingeniero Electricista, Doctor en Informática Industrial

Referencias (VER)

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

Citado por: