Point cloud map construction method based on ground constraint and loop detection

Abstract

High-precision point cloud map is critical for Level 3 and above autonomous vehicles to operate effectively. When autonomous vehicles only use LiDAR to construct the point cloud map, drift deviation will occur in the horizontal and vertical directions, which affects the quality of the constructed map. To solve this issue, this paper proposed a Simultaneous Localization and Mapping (SLAM) framework for outdoor autonomous vehicles based on LiDAR. Firstly, the ground constraint was constructed by the extracted ground point cloud and the standard ground normal vector. Secondly, the preliminary pose was generated by the feature point cloud pose constraint and the ground constraint combined with the uniformly accelerated motion model. Finally, the preliminary pose and similar scene pose transformation detected in loop closure detection were added into the pose graph, and an optimized pose was obtained to construct the point cloud map. The proposed method was evaluated on the MulRan dataset and real scene. The results showed that the proposed method can achieve the positioning deviation within 0.86m and 3.1m respectively in the 3.3km and 7.7km motion range. It was superior to the comparison algorithm in local accuracy and global consistency.