A fast LiDAR place recognition and localization method by fusing local and global search

摘要

? 2023 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)Place recognition is an important branch of Simultaneous Localization and Mapping (SLAM), which finds revisited places, thereby reducing error accumulations. Most mainstream methods only concentrate on the similarity of two LiDAR scans and cannot obtain the pose information. In this paper, we design a descriptor named Occupied Place Description (OPD) and propose an efficient map-aided place recognition and localization method. Our method can effectively detect loop events, obtain the vehicle's poses, and overcome the overlap dependence of pairwise point cloud registration. It is composed of four modules. First, the map data generation is designed to store the map database as binary files. Then, a trajectory initialization consisting of global search, local search, and trajectory score computation is proposed to determine the vehicle's initial poses. Next, an online localization method is developed by combining descriptor similarity and Kalman Filtering, which only takes less than 5ms per localization. Finally, loop events are detected based on pose information. Exhaustive evaluations on KITTI and MulRan datasets demonstrate the superiority of our method. The average absolute trajectory error (ATE) is 0.5 m and the average relative rotation error is 2.7 degrees. F1 score measures the success rate of place recognition and our average maximum F1 score achieves nearly 0.987. The source code will be available in https://github.com/ShiPC-AI/Occupied-Place-Description.