Segregator: Global Point Cloud Registration with Semantic and Geometric Cues

TL;DR

Segregator introduces a robust global point cloud registration method that combines semantic and geometric cues to improve outlier rejection and handle degenerate cases effectively.

Contribution

It proposes a novel degeneracy-robust correspondence method and G-TRIMs for improved outlier detection in point cloud registration.

Findings

Outperforms existing methods on real-world datasets.

Effectively handles degenerate and unconstrained cases.

Provides robust and efficient registration results.

Abstract

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.