Point cloud rigid registration method based on local Poisson curved surface reconstruction

Abstract

The invention discloses a point cloud rigid registration method based on local sample Poisson curved surface reconstruction to find a corresponding point, and belongs to the field of digital design and manufacturing. The method is characterized by, in crude registration, mutually selecting feature point pairs from a floating point cloud and a fixed point cloud, and establishing a Poisson curved surface based on a neighborhood point set of feature points of the fixed point cloud; establishing a KD tree of the curved surface; searching a closest point of a sample point of the floating point cloud in the KD tree, and serving the closest point as a reference point; serving the closest point from the sample point to a reference point ring domain surface patch as the corresponding point, and establishing a measure function based on a corresponding point pair and calculating transformation parameters through an SVD method; and on the basis of crude registration, in precise registration, obtaining feature point pairs in a self-adaptive manner based on a public domain, and establishing error metric by utilizing the minimum distance from the point to the Poisson curved surface, so that transformation parameters can be calculated, and registration precision is further improved. In crude registration, higher registration precision can be achieved, and the precise registration can quickly converge to the global optimum and has higher robustness.

Description

technical field [0001] The invention provides a point cloud rigid registration method based on partial Poisson surface reconstruction, which can be used for registration of multi-view point cloud data of sampling data on the surface of a physical object, and belongs to the field of digital design and manufacturing. Background technique [0002] In the fields of reverse engineering, computer graphics, quality inspection, etc., it is necessary to obtain 3D point cloud data from the surface of the object. Due to the occlusion of the surface of the object and the limitation of the measurement range of the scanning equipment, the current mainstream laser measurement equipment and grating projection measurement equipment must scan the measured object from multiple angles and sub-regions to obtain all point cloud data on the surface of the object. However, during the transformation angle scanning process, the coordinate system of the point cloud data obtained by each scan is differ...

AI Technical Summary

Problems solved by technology

Rusu et al. "Semantic 3d object maps for everyday manipulation in human living environments" (KI-Künstliche Intelligenz, 2010, 24(4): 345-348.) proposed feature descriptors based on FPFH for feature point matching, and added feature point discrimination conditions , the identified candidate feature points are further excluded by geometric information such as curvature and normal, and the unsuitable feature points are eliminated. The whole process uses the KD tree to speed up the search. This method has achieved a very good registration effect. Therefore, the The method is adopted by the open source point cloud algorithm library PCL, but this method still does not solve the drawbacks of excessive dependence on the initial relative position. As long as the initial relative position of the two point cloud models is not ideal, the registration will fail

Various improved methods improve the registration accuracy by finding more accurate geometric primitives and establish more efficient error estimation rules to increase the speed of convergence, but the problems brought about are complex calculations, high time costs, and the point clouds processed by each method Data is often specific and not universal

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