Rigid 3D Point Cloud Registration Based on Point Feature Histograms
Xi Wang, Xutang Zhang
Available Online June 2016.
- https://doi.org/10.2991/mecs-17.2017.99How to use a DOI?
- 3D point cloud, 3D registration, rigid transformation, Iterative Closest Point(ICP), Point Feature Histograms(PFH)
- 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.
- 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 -