Mr. Mikhail Turov Profile

Mikhail Turov

at Chelyabinsk State Univ.

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Proceedings Article | 3 October 2022 Poster + Paper

Clustering in coarse registration task and extraction of common parts of point clouds

Sergei Voronin, Artyom Makovetskii, Vitaly Kober, Aleksei Voronin, Mikhail Turov

Proceedings Volume 12226, 122261F (2022) https://doi.org/10.1117/12.2633548

KEYWORDS: Clouds, Matrices, 3D modeling, Data modeling, Reconstruction algorithms, Machine vision, Databases, Computer vision technology, Computer simulations, 3D vision

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Point cloud registration is an important method in 3D point cloud processing, which is used in computer vision, autonomous driving, and other fields. Point cloud registration looks for the optimal rigid transformation that can align two input point clouds to a common coordinate system. The most common method of alignment using geometric characteristics is the Iterative Closest Point (ICP) algorithm. The disadvantage of classical ICP variants, such as pointto-point and point-to-plane, is their dependence on the initial placement of point clouds. If the rotation that can align two point clouds is sufficiently large, the ICP algorithm can converge to a local minimum. Coarse point clouds registration algorithms are used to find a suitable initial alignment of two clouds. In particular, feature-based methods for coarse registration are known. In this paper, we propose an algorithm to extract the common parts of the incongruent point clouds and coarsely aligning them. We use the SHOT algorithm to find a match between two point clouds. The corresponding neighborhoods are obtained by the correspondence between points. The neighborhoods define local vector bases that allow computing an orthogonal transformation. The proposed algorithm extracts common parts of incongruent point clouds. Computer simulation results are provided to illustrate the performance of the proposed method.

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