用最大团搜索替代RANSAC，提升3D激光雷达建图的回环检测鲁棒性。
Loop Closure via Maximal Cliques in 3D LiDAR-Based SLAM
- 构建特征对应兼容图，通过最大团搜索验证回环，避免随机采样。
- 在稀疏或模糊环境下，位姿误差降低23.5%，回环检测更可靠。
- 适用于多种激光雷达数据集，也适用于2D投影地图，通用性强。
可靠的回环检测仍是3D激光雷达SLAM中的关键挑战，尤其在传感器噪声、环境模糊和视角变化条件下。传统RANSAC方法虽用于几何模型拟合，但在异常值存在时可能失效，导致地图不一致。本文提出一种新的确定性算法CliReg，将回环验证替换为特征对应兼容图上的最大团搜索，避免随机采样，增强对噪声和异常值的鲁棒性。我们将该方法集成到实时管道中，采用二进制3D描述子与汉明距离嵌入的二叉搜索树匹配。在多个包含不同激光雷达传感器的真实数据集上评估，结果表明所提方法在位姿误差上持续低于RANSAC，尤其在稀疏或模糊条件下表现更优。额外实验在基于2D投影的地图上验证了其跨空间域的通用性，证明该方法是回环检测的一种高效稳健替代方案。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。