BEVLOC: End-to-End 6-DoF Localization Via Cross-Modality Correlation Under Bird’s Eye View

Accurate ego-centric localization assumes a paramount significance in the domain of autonomous driving. However, traditional methods for camera-LiDAR map localization rely on perspective projection to create a unified representation, which often falls short due to challenges such as occlusion and the sparse nature of point cloud data. Despite the recent surge in popularity of the Bird's-Eye-View (BEV) paradigm within autonomous driving, its potential applications in localization tasks have remained relatively underexplored. In response to this concern, this paper presents a pioneering end-to-end approach called the BEV Localization Network via LiDAR Map (BEVLoc). By fusing the image and LiDAR map in the BEV space via the concept of optical flow-based correlation, the BEVLoc framework can leverage the synergistic power of cross-modalities in localizing the vehicle. Experimental results conducted on the KITTI dataset highlight the efficacy and performance of BEVLoc in the realm of autonomous vehicle localization.