© 2015 IEEE. Vehicle localization in large-scale urban environments has been commonly addressed as a map-matching problem in the literature. Generally, the maps are 2D images of the world where each pixel covers a part of it. However, building maps for large-scale urban environments requires driving the vehicle along the desired path at least once. In order to simplify this task, in this work, we propose a new localization system that uses satellite aerial map-images available on the Internet to localize a vehicle in a complex urban environment. Satellite aerial map-images are compared against re-emission maps built from the infrared reflectance information of the vehicle's LiDAR. Normalized Mutual Information (NMI) is used to compare re-emission and aerial map images. A Particle Filter Localization strategy is applied for vehicle's localization. As a result, the system has an accuracy of 0.89m in a test course with 6.5km. Our system can be used continuously without losing track, and it works even in dark and partially occluded areas.
Veronese, L. D. P., De Aguiar, E., Nascimento, R. C., Guivant, J., Cheein, F. A. A., De Souza, A. F., & Oliveira-Santos, T. (2015). IEEE International Conference on Intelligent Robots and Systems. 4285-4290. Paper presented at conference, . https://doi.org/10.1109/IROS.2015.7353984