LiDAR-inertial SLAM Algorithm Based on Point Cloud Structure and Appearance

To enhance precise navigation capabilities of mobile robots operating in the satellite-denied environments and to mitigate accumulated positioning error in the large-scope motion scenarios, a LiDAR (light detection and ranging) -inertial SLAM (simultaneous localization and mapping) algorithm based on point cloud structure and appearance is proposed. The proposed algorithm consists of two parts: the LiDAR-inertial odometry considering the point cloud structure, and the loop closure detection and optimization based on the point cloud appearance. In the part of LiDAR-inertial odometry, navigation states are predicted by IMU (inertial measurement unit) information, a direct alignment observation equation is constructed with the point cloud structure considered, and navigation states are updated in real-time by an iterative error extended Kalman filter. In the part of loop closure detection and optimization, keyframes are identified by evaluating discrepancies in point cloud appearance, relative motion poses and time constraints. Then, candidate loop keyframes are screened out based on point cloud appearances. The candidate loop keyframes are subsequently sorted according to the appearance matching distances and the two-dimensional distances. Finally, the loop keyframe is confirmed and the pose graph is constructed for the global pose optimization and the map adjustment. Autonomous navigation experiments in Xi'an Expo Park show that autonomous navigation is realized in real-time, the loops are accurately detected in the loop motion, and the loop optimization is effectively completed by the proposed algorithm. The average error between the starting and ending points of trajectories is only 0.07 m. Moreover, a multimodal integrated navigation switching method in the LiDAR degradation environments is further discussed, which effectively improves the reliability of autonomous navigation.