GPC-LIO-SAM: Lidar inertial odometry and mapping method for mobile robot with ground plane constraint

Due to the limitation of the vertical angle resolution of the lidar sensor, the observation information in the vertical direction is reduced, resulting in the elevation cumulative drift phenomenon of many lidar SLAM algorithms in large-scale environment, which reduces the accuracy of pose estimation and the quality of mapping. In order to solve this problem, based on LIO-SAM, this paper proposes a lidar SLAM algorithm with ground plane constraint. The method can effectively extract the ground point cloud and obtain the plane normal vector, and construct the constraint relationship with the prior plane normal vector. The optimal state estimation is solved by nonlinear optimization method, which effectively reduces the elevation drift caused by the long-term operation of the system in large-scale environment. This paper compares the improved algorithm with A-LOAM, LeGO-LOAM and original LIO-SAM algorithms in challenging datasets such as KITTI, M2DGR and UrbanNav. The results show that this method effectively improves the accuracy of pose estimation and the quality of mapping, and limit the elevation drift of mobile robot in ground operation, which has high application value.