A modified iterative closest point algorithm for noisy data

Dmitrii Tihonkih; Artyom Makovetskii; Aleksei Voronin

doi:10.1117/12.2274139

19 September 2017 A modified iterative closest point algorithm for noisy data

Dmitrii Tihonkih, Artyom Makovetskii, Aleksei Voronin

Proceedings Volume 10396, Applications of Digital Image Processing XL; 103962W (2017) https://doi.org/10.1117/12.2274139
Event: SPIE Optical Engineering + Applications, 2017, San Diego, California, United States

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.

Citation Download Citation

Dmitrii Tihonkih, Artyom Makovetskii, and Aleksei Voronin "A modified iterative closest point algorithm for noisy data", Proc. SPIE 10396, Applications of Digital Image Processing XL, 103962W (19 September 2017); https://doi.org/10.1117/12.2274139

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