A 3D point cloud registration method based on weighted principal component analysis and m estimation

Abstract

The invention discloses a three-dimensional point cloud registration method based on a weighted principal component analysis method and M estimation, and the method comprises the steps of firstly, obtaining a rough and initial conversion relation through a weighted PCA algorithm, and achieving the rough registration of an original point cloud and a target point cloud; then, in order to quickly obtain an accurate rotation translation matrix, using the BP neural network and the two-dimensional moving window for simplifying the number of point clouds of the rotation translation matrix; and finally, adopting a Cauchy function with resistance to noise as an objective function, calculating an accurate alignment relation according to an ICP algorithm, and realizing the accurate registration. According to the method, the time, the space complexity and the algorithm complexity of the registration algorithm can be effectively reduced, and an accurate conversion relation can be obtained for the original point cloud containing noise and abnormal points.

Description

technical field [0001] The invention belongs to the technical field of three-dimensional reconstruction, and in particular relates to a three-dimensional point cloud registration method based on weighted principal component analysis and M estimation. Background technique [0002] With the rapid development of depth sensing equipment, the research object of computer vision technology has gradually converted from two-dimensional images and LIDAR scanning data to three-dimensional point cloud data. Due to the limitation of the measurement range of the site or the measuring instrument, a three-dimensional object is often presented as several fragments of point cloud data from different perspectives. In order to obtain the complete point cloud data of the object, it needs to be transformed into the same coordinate system through the registration method. Point cloud registration methods can be divided into coarse registration and fine registration. The main purpose of coarse reg...

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Problems solved by technology

Because PCA pays attention to the overall structural characteristics, the distance difference used in the implementation is the difference between the point cloud data and the center of gravity of the point cloud, ignoring the irregularity of the shape of the three-dimensional object itself, and unable to take into account the local structural characteristics of each point in the point cloud

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