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：本文介绍一个全球点云登记框架,它利用语义信息和几何分布法,有效地建立外部-紫外线通信和搜索内线。目前最先进的算法依靠点特征来建立模拟通信并通过使用对称的距离一致性检查加以完善。然而,这种计划存在衰败的情况,即地方点特征的描述能力降低分级,以及不受限制的情况,在这种情况下,基于时间保留(l-TRIMS)的检查无法充分限制目前的观察是否与其他观察一致,从而导致复杂的NP-完整问题需要解决。为了解决这些问题,我们提出了一个新的脱精度-紫外线和高效的相应程序,由实例一级的语义集群和几何等分级点特征组成。在另一方面,建议高斯分布式的翻译和轮用变量测量(G-TRIMS)进行一致性检查,并进一步限制问题大小。我们验证了我们关于广泛基于真实世界数据实验的拟议算法。