Proceedings of the 2015 AASRI International Conference on Circuits and Systems

Registration of Point Cloud Data Based on PSO

Authors
Han Xianquan, Nie Zan, Zhang Zhen, Zhou Li
Corresponding Author
Han Xianquan
Available Online August 2015.
DOI
10.2991/cas-15.2015.53How to use a DOI?
Keywords
point cloud; registration; PSO; cross products of normal vector.
Abstract

At present,3-D model can be build through point clouds data which is quickly get by laser scanning technology. Most of the researches on registration problems are focused on feature matching method. This paper discusses a method with global optimization algorithm. In order to solve the problem efficiently, we present a method using PSO algorithm with Cross Products of Normal Vector. This method does not require artificial targets and feature points frequently. The fitness function is used the sum of normal vectors’ cross products to define, and it applies an efficient “Universal Search” and implements cloud data registration based on the best registration criterion. The application and analysis on example indicates the proposed method is effective and feasible.

Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2015 AASRI International Conference on Circuits and Systems
Series
Advances in Computer Science Research
Publication Date
August 2015
ISBN
978-94-62520-74-5
ISSN
2352-538X
DOI
10.2991/cas-15.2015.53How to use a DOI?
Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Han Xianquan
AU  - Nie Zan
AU  - Zhang Zhen
AU  - Zhou Li
PY  - 2015/08
DA  - 2015/08
TI  - Registration of Point Cloud Data Based on PSO
BT  - Proceedings of the 2015 AASRI International Conference on Circuits and Systems
PB  - Atlantis Press
SP  - 222
EP  - 224
SN  - 2352-538X
UR  - https://doi.org/10.2991/cas-15.2015.53
DO  - 10.2991/cas-15.2015.53
ID  - Xianquan2015/08
ER  -