无人车蛙跳协同的激光 SLAM 退化校正

Stable and high-precision localization is a prerequisite for realizing the cooperative autonomous navigation of unmanned ground vehicle (UGV) . LiDAR simultaneous localization and mapping (SLAM) often fails to achieve the precise localization in scenarios lacking geometric features, such as corridors, tunnels,and deserts. Therefore, a leapfrog cooperative LiDAR SLAM degradation correction method is proposed for UGVs. This method is used to estimate the normal vector of each feature point in the current frame, and a LiDAR SLAM degradation detection algorithm is devised. When the degradation of environment is detected, the ranging information about two unmanned vehicles is utilized to correct the degradation in LiDAR SLAM. Finally,the locating results are further optimized in the pose graph. Testing on two self-built UGV platforms reveals that the proposed method achieves better mapping performance compared to the current famous LiDAR SLAM methods,demonstrating its significant capability to enhance the locating performance of LiDAR SLAM in degraded scenarios.