Loop Closure via Maximal Cliques in 3D {LiDAR}-Based {SLAM}

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.