Research on 3D point cloud registration algorithm based on FPFH and ColorICP

With the rapid development of 3D reconstruction, point cloud registration technology, a key step in 3D data processing, has garnered significant attention. Existing 3D point cloud registration technologies face issues such as low matching rates in coarse registration, misidentification of feature points, lengthy registration times, and low registration accuracy. Consequently, an improved registration algorithm that combines Fast Point Feature Histogram (FPFH) for coarse registration and Colored Iterative Closest Point (ColorICP) for fine registration has been proposed. The process begins with pre-processing the point cloud through downsampling filtration; next, point cloud features are extracted using FPFH to achieve feature matching and obtain the initial transformation matrix; finally, ColorICP is used for fine point cloud registration. Experiments on the Stanford bunny dataset from the standard point cloud library and real-world point cloud data demonstrate that the proposed registration algorithm effectively utilizes both color and geometric features of the point cloud, achieving significant improvements in registration accuracy and duration compared to traditional registration algorithms.