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

TL;DR

This paper presents CliReg, a deterministic maximal clique-based algorithm for loop closure detection in 3D LiDAR SLAM, offering improved robustness and accuracy over RANSAC in noisy and ambiguous environments.

Contribution

Introduces CliReg, a novel deterministic loop closure validation method using maximal clique search, replacing RANSAC for enhanced robustness in LiDAR SLAM.

Findings

Lower pose error compared to RANSAC

More reliable loop closures in noisy conditions

Effective across diverse LiDAR sensors and environments

Abstract

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.