Odometry-Assisted LiDAR-OpenStreetMap Matching Method for Vehicle Global Positioning

Global vehicle positioning through real-time LiDAR perception matched with online digital maps like OpenStreetMap (OSM) offers a promising solution to reduce reliance on high-quality maps in urban scenarios. However, the accuracy of global positioning based on LiDAR–OSM matching suffers from imprecise planar spatial information in OSM and frame-to-frame inconsistencies with the road network, which limits LiDAR–OSM matching to serving only as global position initialization rather than continuous positioning. To extend the application scenarios and performance of global positioning using OSM, a LiDAR–OSM matching framework is proposed to solve the planar association problem between real-time local perception and corresponding global position initialization sequences. Specifically, the motion consistency assumption is used to extract and reconstruct global candidate sequences that satisfy temporal and spatial continuity, which serves as the foundation for sequence-to-sequence matching with the local odometry. Furthermore, an online sequence-to-sequence transformation matrix estimation and verification mechanism is proposed to obtain a local-to-global solution that is subject to the activity range of vehicles and achieves optimal consistency with the road network. The performance of the proposed method is validated under different odometries and global initialization position confidence in the KITTI dataset. The results demonstrate that the proposed method achieves sub-meter level precision, showing a significant improvement over other LiDAR–OSM positioning methods in most urban sequences.