  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.
