A Robust Lidar SLAM System Based on Multi-Sensor Fusion

In this paper, we propose a LiDAR-based multi-sensor fusion SLAM system that integrates magnetometer, odometer and IMU information to solve the problem of accuracy degradation of lidar SLAM algorithm in scenes with insufficient structural features. In the lidar odometer part, based on the feature-based point cloud matching algorithm, magnetometer and odometer constraints are introduced to improve the robustness of the algorithm. At the back end, we constructed a factor graph for the global pose optimization, and added the measurement information of each sensor into the factor graph as a factor, so as to realize the nonlinear optimization of the pose and IMU bias. Experimental results show that the proposed algorithm has good robustness and accuracy, and is superior to LeGO-LOAM algorithm in positioning error.