{"id":38970,"date":"2026-08-21T15:27:30","date_gmt":"2026-08-21T15:27:30","guid":{"rendered":"https:\/\/lamarr-institute.org\/publication\/efficiently-closing-loops-in-lidar-based-slam-using-point-cloud-density-maps\/"},"modified":"2026-09-17T11:17:22","modified_gmt":"2026-09-17T11:17:22","slug":"efficiently-closing-loops-in-lidar-based-slam-using-point-cloud-density-maps","status":"publish","type":"publication","link":"https:\/\/lamarr-institute.org\/de\/publication\/efficiently-closing-loops-in-lidar-based-slam-using-point-cloud-density-maps\/","title":{"rendered":"Efficiently closing loops in {LiDAR}-based {SLAM} using point cloud density maps"},"content":{"rendered":"<p>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\u2019s-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}               .<\/p>\n","protected":false},"excerpt":{"rendered":"<p>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 [&hellip;]<\/p>\n","protected":false},"author":14,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[30],"class_list":["post-38970","publication","type-publication","status-publish","hentry","publication-type-article"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/38970","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication"}],"about":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/types\/publication"}],"author":[{"embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/users\/14"}],"version-history":[{"count":0,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/38970\/revisions"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=38970"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=38970"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}