Computational Analysis of Distance Operators for the Iterative Closest Point Algorithm

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Title: Computational Analysis of Distance Operators for the Iterative Closest Point Algorithm
Authors: Mora, Higinio | Mora Pascual, Jerónimo Manuel | Garcia-Garcia, Alberto | Martínez González, Pablo
Research Group/s: Informática Industrial y Redes de Computadores
Center, Department or Service: Universidad de Alicante. Departamento de Tecnología Informática y Computación
Keywords: Iterative Closest Point algorithm | Computational analysis | Distance operators
Knowledge Area: Arquitectura y Tecnología de Computadores
Issue Date: 21-Oct-2016
Publisher: Public Library of Science (PLoS)
Citation: Mora H, Mora-Pascual JM, García-García A, Martínez-González P (2016) Computational Analysis of Distance Operators for the Iterative Closest Point Algorithm. PLoS ONE 11(10): e0164694. doi:10.1371/journal.pone.0164694
Abstract: The Iterative Closest Point (ICP) algorithm is currently one of the most popular methods for rigid registration so that it has become the standard in the Robotics and Computer Vision communities. Many applications take advantage of it to align 2D/3D surfaces due to its popularity and simplicity. Nevertheless, some of its phases present a high computational cost thus rendering impossible some of its applications. In this work, it is proposed an efficient approach for the matching phase of the Iterative Closest Point algorithm. This stage is the main bottleneck of that method so that any efficiency improvement has a great positive impact on the performance of the algorithm. The proposal consists in using low computational cost point-to-point distance metrics instead of classic Euclidean one. The candidates analysed are the Chebyshev and Manhattan distance metrics due to their simpler formulation. The experiments carried out have validated the performance, robustness and quality of the proposal. Different experimental cases and configurations have been set up including a heterogeneous set of 3D figures, several scenarios with partial data and random noise. The results prove that an average speed up of 14% can be obtained while preserving the convergence properties of the algorithm and the quality of the final results.
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0164694
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2016 Mora et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Peer Review: si
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Appears in Collections:INV - I2RC - Artículos de Revistas

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