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



Palabras clave:

vehículos autónomos, localización de robots, filtros de Kalman, radar láser


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.


Los datos de descargas todavía no están disponibles.

Biografía del autor/a

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


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. DOI:

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.

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. DOI:

Cui, Y., & Ge, S. S. (2003). Autonomous Vehicle Positioning With GPS in. 19 (1), 15–25. DOI:

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. DOI:

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. DOI:

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. DOI:

Hata, A., Osorio, F., & Wolf, D. (2014). Robust curb detection and vehicle localization in urban environments. IEEE Intelligent Vehicles Symposium, Proceedings, Iv, 1257–1262. DOI:

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. DOI:

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. DOI:

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. DOI:

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. DOI:

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. DOI:

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. DOI:

Qu, X., Soheilian, B., & Paparoditis, N. (2018). Landmark based localization in urban environment. ISPRS Journal of Photogrammetry and Remote Sensing, 140, 90–103. DOI:

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:

Schlichting, A., & Brenner, C. (2014). Localization using automotive laser scanners and local pattern matching. IEEE Intelligent Vehicles Symposium, Proceedings, Iv, 414–419. DOI:

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. DOI:

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. DOI:

Thrun, S., Burgard, W., & Fox, D. (2006). Probabilistic robotics. DOI:

United Nations. (2019). World Urbanization Prospects: The 2018 Revision. DOI:

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. DOI:

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. DOI:

Wang, L., Zhang, Y., & Wang, J. (2017). Map-Based Localization Method for Autonomous Vehicles Using 3D-LIDAR. IFAC-PapersOnLine, 50 (1), 276–281. DOI:

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. DOI:

Zhou, W., Worrall, S., Zyner, A., & Nebot, E. (2018). Automated Process for Incorporating Drivable Path into Real-time Semantic Segmentation. 6039–6044. DOI:



Cómo citar

Posso-Bautista, B., Bacca-Cortés, E. B., & Caicedo-Bravo, E. (2022). Autonomous vehicle localization method based on an extended Kalman filter and geo-referenced landmarks. Revista De Investigación, Desarrollo E Innovación, 12(1), 121–136.