ROLL: Long-Term Robust LiDAR-based Localization With Temporary Mapping in Changing Environments

Long-term scene changes pose challenges to localization systems using a pre-built map. This paper presents a LiDAR-based system that provides robust localization against those challenges. Our method starts with activation of a mapping process temporarily when global matching towards the pre-built map is unreliable. The temporary map will be merged onto the pre-built map for later localization sessions once reliable matching is obtained again. We further integrate a LiDAR inertial odometry (LIO) to provide motion-compensated LiDAR scans and a reliable pose initial estimate for the global matching module. To generate a smooth real-time trajectory for navigation purposes, we fuse poses from odometry and global matching by solving a pose graph optimization problem. We evaluate our localization system with extensive experiments on the NCLT dataset including a variety of changing indoor and outdoor environments, and the results demonstrate a robust and accurate long-term localization performance. The implementations are open sourced on GitHub
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https://github.com/HaisenbergPeng/ROLL.