Coarse Point Cloud Registration Based on Variational Functionals

Artyom Makovetskii (), Sergei Voronin, Vitaly Kober and Alexei Voronin
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Artyom Makovetskii: Department of Mathematics, Chelyabinsk State University, 454001 Chelyabinsk, Russia
Sergei Voronin: Department of Mathematics, Chelyabinsk State University, 454001 Chelyabinsk, Russia
Vitaly Kober: Department of Mathematics, Chelyabinsk State University, 454001 Chelyabinsk, Russia
Alexei Voronin: Department of Mathematics, Chelyabinsk State University, 454001 Chelyabinsk, Russia

Mathematics, 2022, vol. 11, issue 1, 1-25

Abstract: Point cloud collection forming a 3D scene typically uses information from multiple data scans. The common approach is to register the point cloud pairs consequentially using a variant of the iterative closest point (ICP) algorithm, but most versions of the ICP algorithm only work correctly for a small movement between two point clouds. This makes it difficult to accumulate multiple scans. Global registration algorithms are also known, which theoretically process point clouds at arbitrary initial positions. Recently, a multiparameter variational functional was described and used in the ICP variant to register point clouds at arbitrary initial positions. The disadvantage of this algorithm was the need for manual selection of parameters. In this paper, a modified version of the algorithm with automatic selection of the model parameters is proposed. The proposed algorithm is a fusion of the ICP and RANSAC concepts. Moreover, the algorithm can be parallelized. The performance of the proposed algorithm is compared with that of known global registration algorithms.

Keywords: point cloud registration; variational functional; global registration; coarse registration; closed-form solution; iterative closest points (ICP); orthogonal transformation; surface reconstruction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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