Abstract
Three-dimensional (3D) point cloud registration is an important step in three-dimensional (3D) model reconstruction or 3D mapping. 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 fast method of global registration, which is based on RGB (Red, Green, Blue) value by using the four initial point pairs (FIPP) algorithm. First, the number of different RGB values of points in a dataset are counted and the colors in the target dataset having too few points are discarded by using a color filter. A candidate point set in the source dataset are then generated by comparing the similarity of colors between two datasets with color tolerance, and four point pairs are searched from the two datasets by using an improved FIPP algorithm. Finally, a rigid transformation matrix of global registration is calculated with total least square (TLS) and local registration with the iterative closest point (ICP) algorithm. The proposed method (RGB-FIPP) has been validated with two types of data, and the results show that it can effectively improve the speed of 3D point cloud registration while maintaining high accuracy. The method is suitable for points with RGB values.
Highlights
Three-dimensional (3D) point cloud registration is very important in 3D point cloud data processing, which can provide support for post-processing such as feature extraction, 3D modeling, object recognition, etc
In which includes thethe results of of four initial point pairs, global registration with value by using the improved algorithm, four initial point pairs, global registration with RGB value by using the improved FIPP algorithm, and local discuss thethe influence of parameters andand the and local registration registrationby byusing usingthe theICP
We presented a fast method for 3D point cloud registration by using RGB value of the point, which is called RGB-FIPP
Summary
Three-dimensional (3D) point cloud registration is very important in 3D point cloud data processing, which can provide support for post-processing such as feature extraction, 3D modeling, object recognition, etc. Point cloud registration is the process of looking for an optimal rigid transformation matrix so that source point cloud Q can be transformed into Q’ and the overlapping regions of P and Q’ are as close as possible. The ICP algorithm solves the transformation matrix by using pairs of nearest 3D points in the target and source datasets as correspondences, and transforms the original point dataset into new coordinates by using the matrix. It repeats the above steps until the accuracy requirements are satisfied. The ICP algorithm can achieve high registration accuracy, Sensors 2020, 20, 138; doi:10.3390/s20010138 www.mdpi.com/journal/sensors
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