A Point Cloud Automatic Registration Method Based on Normal Vector

Abstract

The invention relates to a point cloud automatic registration method based on normal vector. According to the method, processing objects are two or more than two pieces of three-dimensional point cloud data, wherein overlapped part exists every two pieces of adjacent three-dimensional point cloud data. The method comprises the following processing steps that (1) feature points are selected according to the point cloud local normal vector changes; (2) the histogram feature quantity is designed for carrying out feature description on each obtained feature point; (3) the initial matching dot pair is obtained through comparing the histogram feature vector of the feature points; (4) the precise matching dot pair is obtained through applying the rigid distance constraint condition and combining a RANSAC (random sample consensus) algorithm, and in addition, the initial registration parameters are obtained through calculation by using a four-element method; and (5) an improved ICP (iterative closest point)) algorithm is adopted for carrying out point cloud precise registration. The point cloud can be automatically registered according to the steps. The method has the advantages that feature description is simple, identification degree is high, higher robustness is realized, and registration precision and speed are improved to a certain degree.

Description

technical field [0001] The invention belongs to the field of three-dimensional information reconstruction, in particular to a point cloud automatic registration method. Background technique [0002] The 3D reconstruction of object surface has always been an important topic in the field of machine vision. The point cloud data on the surface of the measured object can be quickly obtained by an optical scanner, but due to the linear propagation characteristics of light, the complete data on the surface of the measured object needs to be measured multiple times under multiple viewing angles, so that the obtained data are not in the same coordinates Therefore, in order to obtain a complete model of the object, it is necessary to transform the coordinates of the data obtained from each perspective, and finally merge them into a unified coordinate system, which is point cloud registration. Point cloud registration technology has a wide range of applications in many fields such as ...

AI Technical Summary

Problems solved by technology

However, this approximate position transformation makes it difficult to accurately fit the overlapping areas of the depth map, and there are often some interlacing and layering phenomena, which is not conducive to the fusion of subsequent depth map data, so the position of the depth map needs to be further adjusted. To improve the registration accuracy of the depth map, this process is called fine registration

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