Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80429
Title: A three-dimensional point cloud registration based on entropy and particle swarm optimization
Authors: Zhan, X
Cai, Y
He, P 
Keywords: K-d tree
Entropy
Robust
Issue Date: 2018
Publisher: SAGE Publications
Source: Advances in mechanical engineering, 3 Dec. 2018, v. 10, no. 12, p. 1-13, https://doi.org/10.1177/1687814018814330 How to cite?
Journal: Advances in mechanical engineering 
Abstract: A three-dimensional (3D) point cloud registration based on entropy and particle swarm algorithm (EPSA) is proposed in the paper. The algorithm can effectively suppress noise and improve registration accuracy. Firstly, in order to find the k-nearest neighbor of point, the relationship of points is established by k-d tree. The noise is suppressed by the mean of neighbor points. Secondly, the gravity center of two point clouds is calculated to find the translation matrix T. Thirdly, the rotation matrix R is gotten through particle swarm optimization (PSO). While performing the PSO, the entropy information is selected as the fitness function. Lastly, the experiment results are presented. They demonstrate that the algorithm is valuable and robust. It can effectively improve the accuracy of rigid registration.
URI: http://hdl.handle.net/10397/80429
ISSN: 1687-8132
EISSN: 1687-8140
DOI: 10.1177/1687814018814330
Rights: © The Authors(s) 2018
Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License(http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work withoutfurther permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
The following publication Zhan, X., Cai, Y., & He, P. (2018). A three-dimensional point cloud registration based on entropy and particle swarm optimization. Advances in Mechanical Engineering, 10(12), 1-13 is published by Sage and is available at https://dx.doi.org/10.1177/1687814018814330
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