OpenStreetMap-based LiDAR Global Localization in Urban Environment without a Prior LiDAR Map
This enables vehicle localization in areas without pre-existing LiDAR maps, addressing a limitation for autonomous navigation systems, though it is incremental as it builds on existing descriptor-based matching techniques.
The paper tackles vehicle localization in urban environments without prior LiDAR maps by using OpenStreetMap to generate descriptors and matching them with LiDAR data, achieving comparable accuracy to map-based methods and outperforming others on the KITTI dataset sequence 00.
Using publicly accessible maps, we propose a novel vehicle localization method that can be applied without using prior light detection and ranging (LiDAR) maps. Our method generates OSM descriptors by calculating the distances to buildings from a location in OpenStreetMap at a regular angle, and LiDAR descriptors by calculating the shortest distances to building points from the current location at a regular angle. Comparing the OSM descriptors and LiDAR descriptors yields a highly accurate vehicle localization result. Compared to methods that use prior LiDAR maps, our method presents two main advantages: (1) vehicle localization is not limited to only places with previously acquired LiDAR maps, and (2) our method is comparable to LiDAR map-based methods, and especially outperforms the other methods with respect to the top one candidate at KITTI dataset sequence 00.