LIWOM-GD: Enhanced LiDAR-Inertial-Wheel Odometry and Mapping by Fusion with Ground Constraint and Dynamic Points Elimination

The mitigation of the drift of the odometer and the ghost trail effect in the map is a challenge for simultaneous localization and mapping (SLAM). Facing the challenge, a novel method, called LIWOM-GD, is proposed via factor graph optimization, which tightly fuses LiDAR, IMU, and wheel encoder measurements. Firstly, the front-end module provides undistorted LiDAR scans, the pose states to be optimized, and the LiDAR-Inertial (LI) odometry factor. Secondly, the point cloud and pre-integration processing module implements ground segmentation, IMU, and encoder pre-integration to form the ground plane, IMU, and encoder factors. Furthermore, an enhanced dynamic point removal method is integrated into the processing module. Thirdly, a typical loop closure detection method is used to obtain a loop closure factor. Finally, all the above factors are added into the factor graph to obtain optimized results based on the GTSAM library. The proposed SLAM system tightly fuses three sensor measurements based on the factor graph, adding global plane constraint and dynamic points removal module based on spatiotemporal normals. Experimental tests are conducted on both benchmark and self-collected datasets. The results demonstrate that our method not only achieves more robust localization and mapping in degraded environments but also effectively implements dynamic point removal compared with the state-of-the-art SLAM methods.