This paper presents Segregator, a global point cloud registration framework that exploits both semantic information and geometric distribution to efficiently build up outlier-robust correspondences and search for inliers. Current state-of-the-art algorithms rely on point features to set up putative correspondences and refine them by employing pair-wise distance consistency checks. However, such a scheme suffers from degenerate cases, where the descriptive capability of local point features downgrades, and unconstrained cases, where length-preserving (l-TRIMs)-based checks cannot sufficiently constrain whether the current observation is consistent with others, resulting in a complexified NP-complete problem to solve. To tackle these problems, on the one hand, we propose a novel degeneracy-robust and efficient corresponding procedure consisting of both instance-level semantic clusters and geometric-level point features. On the other hand, Gaussian distribution-based translation and rotation invariant measurements (G-TRIMs) are proposed to conduct the consistency check and further constrain the problem size. We validated our proposed algorithm on extensive real-world data-based experiments. The code is available: https://github.com/Pamphlett/Segregator.

翻译：本文提出Segregator，一种利用语义信息与几何分布高效构建抗离群值对应关系并搜索内点的全局点云配准框架。现有最优算法依赖点特征建立假设对应关系，并通过成对距离一致性检验进行优化，但此类方案存在两大缺陷：一是局部点特征描述能力退化时的退化情况；二是基于长度保持的l-TRIMs检验无法充分约束当前观测与其他观测一致性的无约束情况，导致求解NP完全问题的复杂度增加。为解决上述问题，本文一方面提出包含实例级语义聚类与几何级点特征的新型抗退化高效对应构建流程，另一方面提出基于高斯分布的平移旋转不变度量（G-TRIMs）以执行一致性检验并进一步约束问题规模。我们通过大量真实世界数据实验验证了所提算法的有效性。代码地址：https://github.com/Pamphlett/Segregator