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.

翻译：可靠的闭环检测仍然是基于3D LiDAR的SLAM中的关键挑战，尤其是在传感器噪声、环境歧义和视角变化的条件下。RANSAC常被用于存在异常值时的几何模型拟合以实现闭环。然而，该方法可能失效，导致地图不一致。我们提出了一种新颖的确定性算法CliReg，用于闭环验证，该算法通过特征对应兼容性图上的极大团搜索取代了RANSAC验证。此方法避免了随机采样，并在存在噪声和异常值时增强了鲁棒性。我们将该方法集成到采用二进制3D描述符和基于汉明距离嵌入的二叉搜索树匹配的实时流程中。我们在多个包含不同LiDAR传感器的真实世界数据集上进行了评估。结果表明，与RANSAC相比，我们提出的技术始终能实现更低的位姿误差和更可靠的闭环，尤其是在稀疏或模糊的条件下。基于2D投影地图的补充实验证实了其在空间域上的通用性，使得我们的方法成为闭环检测中一种鲁棒且高效的替代方案。