Ground Constrained 3D Lidar SLAM Design and Implementation

Abstract

Synchronized positioning and mapping techniques based on multi-line LIDAR are mainly used for mobile platforms that require high-precision positioning, such as UAVs and self-driving cars. However, multi-line LiDAR is limited by vertical field of view and vertical angular resolution, which can affect the vertical accuracy of attitude estimation. In this paper, we aim to extract the ground information in the environment and fuse it into a factor map to constrain the vertical accuracy of the attitude. Different from the traditional geometric information-based ground plane segmentation, this paper utilizes point cloud distribution information to extract the ground cloud and uses high-confidence ground features to constrain the attitude drift by considering their distribution weights. Finally, the laser range factor, ground constraint factor and closed-loop factor are integrated under the factor graph optimization framework. The effectiveness of the method is validated on a self-constructed dataset.