LAUSR

Simultaneous localization and mapping is an indispensable yet challenging direction for mobile robots. Attracted by 3D LiDAR with accurate depth information and robustness to illumination variations, many 3D LiDAR SLAM methods based on scan-to-map matching have been developed. However, there is a critical issue of existing approaches, where a large and dense map is generally required to achieve satisfactory localization accuracy, leading to low efficiency of scan-to-map matching. To solve this problem, in this letter, we propose a novel 3D LiDAR SLAM based on directed geometry point (DGP) and sparse frame. The former is used to provide a sparse distribution of points in the spatial dimension and the latter gives rise to a sparse distribution of frames in the temporal sequence. The sparsity of points and frames impove the efficiency of 3D LiDAR SLAM, and the strict data association based on directed geometric points also brings in good accuracy of pose estimation. To compensate the accuracy loss of the localization and mapping caused by frame sparsity, point propagation is proposed to improve the quality of directed geometric points in the map and the accuracy of scan-to-map matching. Also, loop detection and pose graph optimization are conducted for global consistency. The experimental results demonstrate the effectiveness of the proposed method in terms of accuracy and efficiency.