基于3D激光雷达的SLAM中通过最大团实现回环闭合
Loop Closure via Maximal Cliques in 3D LiDAR-Based SLAM
AI总结：
本文提出CliReg算法，通过最大团搜索提升3D激光雷达SLAM中回环闭合的鲁棒性和效率。
摘要：
在基于3D激光雷达的SLAM中，可靠的回环闭合检测仍然是一个关键挑战，尤其是在传感器噪声、环境模糊和视角变化条件下。RANSAC通常用于在存在异常值的情况下进行回环闭合的几何模型拟合。然而，这种方法可能会失效，导致地图不一致。我们引入了一种新的确定性算法，CliReg，用于回环闭合验证，该算法用特征对应关系的兼容图上的最大团搜索取代RANSAC验证。这种形式避免了随机采样，并在存在噪声和异常值的情况下增强了鲁棒性。我们将这种方法整合到一个实时管道中，利用二进制3D描述符和基于汉明距离嵌入二进制搜索树的匹配。我们在多个现实世界数据集上进行了评估，这些数据集包含多样化的激光雷达传感器。结果表明，我们的方法在稀疏或模糊条件下 consistently 获得比RANSAC更低的位姿误差和更可靠的回环闭合。此外，在基于2D投影的地图上的额外实验证实了其在空间领域中的通用性，使我们的方法成为回环闭合检测的稳健且高效的替代方案。
英文摘要：
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.
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