BAG-SLAM: A Bundle Adjustment Based Ground LiDAR SLAM System for Outdoor Mobile Robots

Eliminating the drift along the gravity direction is crucial for Simultaneous Localization and Mapping (SLAM) systems in long-term running. In this paper, a novel ground-constrain-based SLAM method (i.e., BAG-SLAM) is proposed to address the drift issue. BAG-SLAM fuses the ground extraction, LiDAR bundle adjustment (BA), and loop-closure detection methods into the back-end. In the back-end, the extracted ground planes of every keyframe are as constrains of robot poses for factor graph optimization. To deal with the ground distortion of scene, LiDAR-BA-based factors are used to refine the ground structure, correct the parameters of ground plane and remove the outliers. Experimental results on several public and self-collected datasets show that the proposed method effectively reduces the pose errors along the gravity direction. BAG-SLAM realizes the more accurate localization and more consistent map construction.