Real-Time 3D Mapping in Complex Environments Using a Spinning Actuated LiDAR System

LiDAR is a crucial sensor for 3D environment perception. However, limited by the field of view of the LiDAR, it is sometimes difficult to achieve complete coverage of the environment with a single LiDAR. In this paper, we designed a spinning actuated LiDAR mapping system that is compatible with both UAV and backpack platforms and propose a tightly coupled laser-inertial SLAM algorithm for it. In our algorithm, edge and plane features in the point cloud are first extracted. Then, for the significant changes in the distribution of point cloud features between two adjacent scans caused by the continuous rotation of the LiDAR, we employed an adaptive scan accumulation method to improve the stability and accuracy of point cloud registration. After feature matching, the LiDAR feature factors and IMU pre-integration factor are added to the factor graph and jointly optimized to output the trajectory. In addition, an improved loop closure detection algorithm based on the Cartographer algorithm is used to reduce the drift. We conducted exhaustive experiments to evaluate the performance of the proposed algorithm in complex indoor and outdoor scenarios. The results showed that our algorithm is more accurate than the state-of-the-art algorithms LIO-SAM and FAST-LIO2 for the spinning actuated LiDAR system, and it can achieve real-time performance.

Automated summary

This paper proposes a spinning actuated LiDAR mapping system and a tightly coupled laser-inertial SLAM algorithm. It improves the stability and accuracy of point cloud registration by extracting edge and plane features and using an adaptive scan accumulation method. By adding LiDAR feature factors and IMU pre-integration factors to the factor graph and jointly optimizing them, the trajectory can be output. An improved loop closure detection algorithm based on the Cartographer algorithm is used to reduce drift. Experimental results show that this algorithm is more accurate and achieves real-time performance compared to existing algorithms.