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• W4383108936 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 (1-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." @default.
• W4383108936 created "2023-07-05" @default.
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• W4383108936 date "2023-05-29" @default.
• W4383108936 modified "2023-10-02" @default.
• W4383108936 title "Segregator: Global Point Cloud Registration with Semantic and Geometric Cues" @default.
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• W4383108936 doi "https://doi.org/10.1109/icra48891.2023.10160798" @default.
• W4383108936 hasPublicationYear "2023" @default.
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