Image and Signal Processing Methods

Improved algorithm for point cloud registration based on fast point feature histograms

[+] Author Affiliations
Peng Li

China University of Mining and Technology, School of Environment Science and Spatial Informatics, No. 1 University Road, Xuzhou 221116, China

Chuzhou University, Anhui Center for Collaborative Innovation in Geographical Information Integration and Application, No. 1 Huifeng West Road, Chuzhou 239000, China

Jian Wang, Yindi Zhao, Yifei Yao

China University of Mining and Technology, School of Environment Science and Spatial Informatics, No. 1 University Road, Xuzhou 221116, China

Yanxia Wang

Chuzhou University, Anhui Center for Collaborative Innovation in Geographical Information Integration and Application, No. 1 Huifeng West Road, Chuzhou 239000, China

Nanjing Normal Univesity, Key Laboratory of Virtual Geographic Environment (Ministry of Education), No. 1 Wenyuan Road, Xianlin University City, Nanjing 219000, China

J. Appl. Remote Sens. 10(4), 045024 (Dec 30, 2016). doi:10.1117/1.JRS.10.045024
History: Received April 27, 2016; Accepted November 30, 2016
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Abstract.  Point cloud registration is very important in three-dimensional (3-D) point cloud data processing as its results directly affect 3-D object reconstruction and other applications. Currently, there are many methods for point cloud registration, but these methods are not able to simultaneously solve the problem of both efficiency and precision. We propose a method of point cloud registration based on fast point feature histogram (FPFH), in which feature points are first extracted from the point cloud dataset according to FPFH and four point-to-point correspondences are found within some given constraints regarding their features, distances, and location relationships. Then, additional point pairs are added on the basis of the initial four point pairs until the number of point pairs satisfies the requirements for point cloud registration. Finally, a rigid transformation matrix is calculated from the correspondence of the point pairs. The results show that there is both a high efficiency and precision in most types of datasets when using this method for point cloud registration.

© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Peng Li ; Jian Wang ; Yindi Zhao ; Yanxia Wang and Yifei Yao
"Improved algorithm for point cloud registration based on fast point feature histograms", J. Appl. Remote Sens. 10(4), 045024 (Dec 30, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.045024


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