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

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.