  LiDAR odometry is one of the essential parts of LiDAR simultaneous
localization and mapping (SLAM). However, existing LiDAR odometry tends to
match a new scan simply iteratively with previous fixed-pose scans, gradually
accumulating errors. Furthermore, as an effective joint optimization mechanism,
bundle adjustment (BA) cannot be directly introduced into real-time odometry
due to the intensive computation of large-scale global landmarks. Therefore,
this letter designs a new strategy named a landmark map for bundle adjustment
odometry (LMBAO) in LiDAR SLAM to solve these problems. First, BA-based
odometry is further developed with an active landmark maintenance strategy for
a more accurate local registration and avoiding cumulative errors.
Specifically, this paper keeps entire stable landmarks on the map instead of
just their feature points in the sliding window and deletes the landmarks
according to their active grade. Next, the sliding window length is reduced,
and marginalization is performed to retain the scans outside the window but
corresponding to active landmarks on the map, greatly simplifying the
computation and improving the real-time properties. 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.
