A LiDAR-OpenStreetMap Matching Method for Vehicle Global Position Initialization Based on Boundary Directional Feature Extraction

OpenStreetMap (OSM) is a low-cost promising alternative to provide the environment representation with global consistency when the pre-built LiDAR maps are not available. However, existing descriptor-based perception-OSM matching methods suffered from significant disparities in dimension and precision compared to the perception and pre-built LiDAR map, leading to degradation of matching accuracy. To improve the accuracy and robustness of matching-based global position initialization using OSM, this article proposes a novel boundary-relative orientation feature descriptor with rotational consistency, facilitating the unified representation of perception and OSM. The proposed scale-free descriptor, derived from the relative changes of boundary trend within the OSM, significantly reduces the reliance on the precision of planar spatial information, thereby promoting the matching accuracy required for global position initialization. Furthermore, the performance of global position initialization with the proposed descriptor is evaluated on KITTI datasets. The results illustrate the proposed descriptor outperforms 3D-3D and 2D-2D descriptor matching-based methods, especially in urban scenarios.