Three-dimensional measurement point cloud optimization registration method

Abstract

The present invention belongs to the technical field of digital manufacturing, and particularly relates to a three-dimensional measurement point cloud optimization registration method. The method comprises: obtaining the source point cloud and the target point cloud; performing denoising preprocessing on the three-dimensional measurement point cloud; using the Markov Monte Carlo-based simulated annealing registration algorithm to solve the global optimal registration transformation matrix; and finally, using the ICP registration method to iteratively complete precise registration. According tothe method provided by the present invention, the problem of convergence to the local optimal solution in the ICP registration method is solved, the global optimization solution of the transformationmatrix in the process of three-dimensional point cloud registration is realized, falling into the local optimum is avoided, the precision of three-dimensional point cloud registration is improved, and the method is superior to the traditional ICP registration; and parameter sampling is realized based on the Markov Monte Carlo method, the convergence speed of the algorithm is accelerated, the accuracy of point cloud registration is improved, the method has strong adaptability to the point cloud, and the algorithm has good robustness.

Description

technical field [0001] The invention belongs to the technical field of digital manufacturing, in particular to a three-dimensional measurement point cloud optimization registration method. Background technique [0002] The rapid development of point cloud data registration technology has expanded from the initial geometric error evaluation and reverse engineering to digital manufacturing fields such as error traceability analysis and precision assembly. The source point cloud must be matched with the target point cloud to obtain an ideal evaluation plan. The registration accuracy is an important guarantee for workpiece positioning and error evaluation. The key point cloud data registration is to solve the transformation matrix between the source point cloud and the target point cloud. Most of the point cloud fine registration adopts the iterative closest point method, that is, the ICP registration method, the core of which is to iteratively find the optimal transformation m...

AI Technical Summary

Problems solved by technology

In this method, through simulated annealing, the problem of converging to the local optimal solution in the ICP registration method is solved, and the global optimization solution of the transformation matrix in the process of 3D point cloud registration is realized to avoid falling into the local optimal solution; through Markov Monte Carlo processing, parameter sampling is realized, and the algorithm convergence speed is accelerated; the adaptability to point clouds is strong, and the algorithm robustness is good

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