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A modified iterative closest point algorithm for noisy data
Author(s): Dmitrii Tihonkih; Artyom Makovetskii; Aleksei Voronin
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Paper Abstract

The problem of aligning of 3D point data is the known registration task. The most popular registration algorithm is the Iterative Closest Point (ICP) algorithm. The traditional ICP algorithm is a fast and accurate approach for rigid registration between two point clouds but it is unable to handle affine case. Recently, extension of the ICP algorithm for composition of scaling, rotation, and translation is proposed. A generalized ICP version for an arbitrary affine transformation is also suggested. In this paper, a new iterative algorithm for registration of point clouds based on the point-to-plane ICP algorithm with affine transformations is proposed. At each iteration, a closed-form solution to the affine transformation is derived. This approach allows us to get a precise solution for transformations such as rotation, translation, and scaling. With the help of computer simulation, the proposed algorithm is compared with common registration algorithms.

Paper Details

Date Published: 19 September 2017
PDF: 7 pages
Proc. SPIE 10396, Applications of Digital Image Processing XL, 103962W (19 September 2017); doi: 10.1117/12.2274139
Show Author Affiliations
Dmitrii Tihonkih, Chelyabinsk State Univ. (Russian Federation)
Artyom Makovetskii, Chelyabinsk State Univ. (Russian Federation)
Aleksei Voronin, Chelyabinsk State Univ. (Russian Federation)


Published in SPIE Proceedings Vol. 10396:
Applications of Digital Image Processing XL
Andrew G. Tescher, Editor(s)

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