LAUSR

For autonomous vehicles, real-time localization and mapping in the unknown environment is very important. In this paper, a fast and lightweight 3D LiDAR simultaneously localization and mapping (SLAM) is presented for the localization of autonomous vehicles in large-scale urban environments. A novel encoding approach based on depth information is proposed to encode unordered point clouds with various resolutions, which avoids missing dimensional information in the projection of point clouds onto a 2D plane. Principal components analysis (PCA) is modified by dynamically selecting neighborhood points according to the encoded depth information to fit the local plane with less time consuming. The threshold and the number of feature points are adaptive according to distance intervals, results in sparse feature points extracted and uniformly distributed in the three-dimensional space. The extracted significant feature points improve the accuracy of the odometer and speed up the alignment of the point cloud. The effectiveness and robustness of the proposed algorithm are verified on the KITTI odometry benchmark and MVSECD. A fast runtime of 21 ms is obtained for the odometer estimation. Compared to several typical state-of-the-art methods on the KITTI odometry benchmark, the proposed approach reduces translation errors by at least 19% and rotation errors by 7.1%.