Efficiently closing loops in {LiDAR}-based {SLAM} using point cloud density maps

Consistent maps are key for most autonomous mobile robots, and they often use {SLAM} approaches to build such maps. Loop closures via place recognition help to maintain accurate pose estimates by mitigating global drift, and are thus key for realizing an effective {SLAM} system. This paper presents a robust loop closure detection pipeline for outdoor {SLAM} with {LiDAR}-equipped robots. Our method handles various {LiDAR} sensors with different scanning patterns, fields of view, and resolutions. It generates local maps from {LiDAR} scans and aligns them using a ground alignment module to handle both planar and non-planar motion of the {LiDAR}, ensuring applicability across platforms. The method uses density-preserving bird’s-eye-view projections of these local maps and extracts {ORB} feature descriptors for place recognition. It stores the feature descriptors in a binary search tree for efficient retrieval, and self-similarity pruning addresses perceptual aliasing in repetitive environments. Extensive experiments on public and self-recorded datasets demonstrate accurate loop closure detection, long-term localization, and cross-platform multi-map alignment, agnostic to the {LiDAR} scanning patterns, fields of view, and motion profiles. We provide the code for our pipeline as open-source software at https://github.com/{PRBonn}/{MapClosures} .