3D LiDAR SLAM for natural guided navigation of automated guided vehicles in unstructured scenes
Ziyang Wang, Xianxing Tang, Haibo Zhou, Linjiao Xiao, Jian Duan
Abstract
Abstract This paper proposes a 3D Light Detection and Ranging (LiDAR) simultaneous localization and mapping system for indoor unstructured scenes to improve the robustness and accuracy of automated guided vehicles. First, in the point cloud preprocessing stage, a curve voxel clustering method considering rarity point clouds is proposed, which effectively distinguishes adjacent objects while handling outliers, and achieves accurate clustering of rarity point clouds. Next, in the feature extraction stage, a curvature evaluation method based on principal component analysis is used to ensure the quality of feature extraction through the invariance of viewpoint and distance. Finally, a two-stage degeneration compensation strategy based on a recursive noise estimation adaptive Kalman filter is proposed, which significantly improves pose estimation accuracy by integrating LiDAR and an inertial measurement unit. Experimental results show that the proposed method achieves outstanding performance on the KITTI dataset, with average relative translation error and rotation error RMSE maintained at 0.7922% and 0.7513 deg/100 m, respectively. Additionally, the method achieves over 40% accuracy improvement in real indoor environments compared to LeGO-LOAM.