LMBAO: A Landmark Map for Bundle Adjustment Odometry in LiDAR SLAM

Existing LiDAR odometry strategies match a new scan iteratively with previous fixed-pose scans, gradually accumulating errors. Furthermore, as an effective joint optimization mechanism, bundle adjustment (BA) cannot be directly introduced into odometry due to the intensive computation of global landmarks. Therefore, this paper designs a landmark map for bundle adjustment odometry (LMBAO) in LiDAR SLAM. First, an active landmark maintenance strategy is developed to obtain a local map of limited size that enables real-time BA. Specifically, this paper keeps entire stable landmarks on the map instead of just their feature points in the sliding window and timely deletes inactive landmarks. Next, unlike visual marginalization to approximates the Gaussian distribution, and a direct and efficient marginalization strategy is performed to retain the scans outside the window to greatly simplifying the computation. Experiments show the effectiveness of LMBAO in outdoor driving. In addition, experiments on three challenging datasets show that our algorithm achieves real-time performance in outdoor driving and outperforms state-of-the-art LiDAR SLAM algorithms, including Lego-LOAM and VLOM.