Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81620
Title: A point cloud registration algorithm based on normal vector and particle swarm optimization
Authors: Zhan, X
Cai, Y
Li, H 
Li, Y 
He, P 
Keywords: K-d tree
Normal vector
Quaternion method
Issue Date: 2019
Publisher: Sage Publications Ltd.
Source: Measurement and control, 2019 How to cite?
Journal: Engineering applications of computational fluid mechanics 
Abstract: Based on normal vector and particle swarm optimization (NVP), a point cloud registration algorithm is proposed by searching the corresponding points. It provides a new method for point cloud registration using feature point registration. First, in order to find the nearest eight neighbor nodes, the k-d tree is employed to build the relationship between points. Then, the normal vector and the distance between the point and the center gravity of eight neighbor points can be calculated. Second, the particle swarm optimization is used to search the corresponding points. There are two conditions to terminate the search in particle swarm optimization: one is that the normal vector of node in the original point cloud is the most similar to that in the target point cloud, and the other is that the distance between the point and the center gravity of eight neighbor points of node is the most similar to that in the target point cloud. Third, after obtaining the corresponding points, they are tested by random sample consensus in order to obtain the right corresponding points. Fourth, the right corresponding points are registered by the quaternion method. The experiments demonstrate that this algorithm is effective. Even in the case of point cloud data lost, it also has high registration accuracy.
URI: http://hdl.handle.net/10397/81620
ISSN: 0020-2940
EISSN: 2051-8730
DOI: 10.1177/0020294019858217
Rights: © The Author(s) 2019. 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 without further 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., Li, H., Li, Y., & He, P. (2019). A point cloud registration algorithm based on normal vector and particle swarm optimization. Measurement and Control, is available at https://doi.org/10.1177/0020294019858217
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