Proceedings of the 2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017)

Rigid 3D Point Cloud Registration Based on Point Feature Histograms

Authors
Xi Wang, Xutang Zhang
Corresponding Author
Xi Wang
Available Online June 2016.
DOI
https://doi.org/10.2991/mecs-17.2017.99How to use a DOI?
Keywords
3D point cloud, 3D registration, rigid transformation, Iterative Closest Point(ICP), Point Feature Histograms(PFH)
Abstract
Depending on the displacement and orientation between point clouds, the registration of scattered point clouds is offten divided into two steps: crude and fine alignment. An approach of point cloud classification based on point feature histogram was proposed in this paper. We propose a method of establishing the point feature histograms to match feature points in different clouds. To reject the outliers, Random Sample Consensus algorithm is used. The rigid transformation matrix in crude alignment is then computed by Singular Value Decomposition method. The golden standard for fine alignment is the Iterative Closest Point algorithm and its variants. In this paper we apply a dynamic constraint of distance to improve the traditional algorithm. The experiment shows that our process of registration works fine with higher accuracy and efficiency.
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This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the 2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017)
Series
Advances in Engineering Research
Publication Date
June 2016
ISBN
978-94-6252-352-4
ISSN
2352-5401
DOI
https://doi.org/10.2991/mecs-17.2017.99How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Xi Wang
AU  - Xutang Zhang
PY  - 2016/06
DA  - 2016/06
TI  - Rigid 3D Point Cloud Registration Based on Point Feature Histograms
BT  - Proceedings of the 2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/mecs-17.2017.99
DO  - https://doi.org/10.2991/mecs-17.2017.99
ID  - Wang2016/06
ER  -