Precise landmark-map for bundle adjustment LiDAR odometry

Abstract

Abstract LiDAR odometry is a critical part of LiDAR simultaneous localization and mapping (SLAM). However, existing methods often suffer from the gradual accumulation of errors. Furthermore, the intensive computational requirements of processing large-scale global landmarks make it impractical to directly introduce bundle adjustment(BA) into real-time odometry. To overcome these challenges, this article presents a new strategy named precise landmark-map for BA odometry. First, BA odometry is optimized by a new active landmark maintenance strategy, aiming to improve local registration accuracy and mitigate error accumulation. Specifically, in contrast to conventional methods that only retain feature points within the sliding window, this paper retains all stable landmarks on the map and removes landmarks based on their level of activity. Moreover, computational efficiency is improved by minimizing the sliding window size and implementing marginalization to maintain scans that are outside the window but associated with active landmarks on the map. In addition, experiments on three challenging datasets validate the real-time performance of our algorithm in outdoor driving scenarios, outperforming state-of-the-art LiDAR SLAM algorithms like Lego-LOAM and VLOM.