{"id":32379,"date":"2026-01-21T17:01:51","date_gmt":"2026-01-21T17:01:51","guid":{"rendered":"https:\/\/lamarr-institute.org\/publication\/loop-closure-via-maximal-cliques-in-3d-lidar-based-slam\/"},"modified":"2026-06-08T13:20:05","modified_gmt":"2026-06-08T13:20:05","slug":"loop-closure-via-maximal-cliques-in-3d-lidar-based-slam","status":"publish","type":"publication","link":"https:\/\/lamarr-institute.org\/de\/publication\/loop-closure-via-maximal-cliques-in-3d-lidar-based-slam\/","title":{"rendered":"Loop Closure via Maximal Cliques in 3D {LiDAR}-Based {SLAM}"},"content":{"rendered":"<p>Reliable loop closure detection remains a critical challenge in 3D {LiDAR}-based {SLAM}, especially under sensor noise, environmental ambiguity, and viewpoint variation conditions. {RANSAC} is often used in the context of loop closures for geometric model fitting in the presence of outliers. However, this approach may fail, leading to map inconsistency. We introduce a novel deterministic algorithm, {CliReg}, for loop closure validation that replaces {RANSAC} verification with a maximal clique search over a compatibility graph of feature correspondences. This formulation avoids random sampling and increases robustness in the presence of noise and outliers. We integrated our approach into a real-time pipeline employing binary 3D descriptors and a Hamming distance embedding binary search tree-based matching. We evaluated it on multiple real-world datasets featuring diverse {LiDAR} sensors. The results demonstrate that our proposed technique consistently achieves a lower pose error and more reliable loop closures than {RANSAC}, especially in sparse or ambiguous conditions. Additional experiments on 2D projection-based maps confirm its generality across spatial domains, making our approach a robust and efficient alternative for loop closure detection.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Reliable loop closure detection remains a critical challenge in 3D {LiDAR}-based {SLAM}, especially under sensor noise, environmental ambiguity, and viewpoint variation conditions. {RANSAC} is often used in the context of loop closures for geometric model fitting in the presence of outliers. However, this approach may fail, leading to map inconsistency. We introduce a novel deterministic algorithm, {CliReg}, for loop closure validation that replaces {RANSAC} verification with a maximal clique search [&hellip;]<\/p>\n","protected":false},"author":12,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[32],"class_list":["post-32379","publication","type-publication","status-publish","hentry","publication-type-inproceedings"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32379","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\/12"}],"version-history":[{"count":0,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32379\/revisions"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=32379"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=32379"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}