Research on 3D LiDAR Outdoor SLAM Algorithm Based on LiDAR/IMU Tight Coupling

Abstract Aiming at the problems of easy loss of GPS positioning signals in outdoor environments and inaccurate map construction and position drift of traditional SLAM algorithms in outdoor scenes, this paper proposes a 3D LiDAR and inertial guidance tightly coupled SLAM algorithm. Firstly, IMU forward propagation is used to predict the current position, then backward propagation is used to compensate the motion distortion in the LiDAR data, and the point cloud alignment residuals are constructed based on the GICP algorithm, and then the Iterative Error State Kalman Filter (IESKF) algorithm is utilized to complete the fusion of the point cloud residuals and the a priori position obtained from the forward propagation of the Inertial Measurement Unit (IMU) to complete the state updating, and then the front-end fusion odometer is constructed. front-end fused odometry. Then, a sparse voxel nearest neighbor structure iVox-based method is used to select key frames and construct local maps to utilize the spatial information during frame-map matching, thus reducing the point cloud alignment time consuming. Then, the front-end odometry and loopback detection factor are used together to complete the global factor map optimization. Finally, the robustness and accuracy of this paper's algorithm are verified in campus field scenes and outdoor open-source dataset KITTI, and compared with the current mainstream algorithms FAST-LIO2 and LIO-SAM. The results show that the algorithm proposed in this paper has smaller cumulative error, higher localization accuracy, and better visualization with higher robustness in the campus scene.