Cross-Modal Monocular Localization in Prior LiDAR Maps Utilizing Semantic Consistency

Abstract

Visual localization for mobile robots and intelligent vehicles in prior LiDAR maps can achieve high accuracy and low cost. However, algorithms for finding the cross-modal correspondences between images and LiDAR map points are not yet stable. In this paper, we propose a monocular visual localization system in prior LiDAR maps, which is based on the cross-modal registration to optimize the camera pose. To align the point clouds from vision and LiDAR map, a point-to-plane Iterative Closest Point algorithm utilizing semantic consistency is designed, and a decoupling optimization strategy is proposed to compute the affine transformation for the monocular scale ambiguity. Experiments on KITTI dataset show that utilizing the semantic consistency and geometric information of the map makes our system competitive with other methods. On the self-collected dataset, experiments on different light intensities demonstrate the robustness of the system in long-term localization tasks, and the ablation study demonstrates the effectiveness of the proposed algorithms.