A Fusion Method of Different Precision 3D Point Cloud Data Based on Mean Shift

Abstract

The invention discloses a fusion method of different-accuracy three-dimension point cloud data based on mean shift. Aiming at two groups of three-dimension point cloud data in different accuracy levels, through utilizing of high-accurate point cloud, error distribution of low-accuracy point cloud is set up, and the mean shift is carried out on the low-accuracy point cloud to eliminate shift errors of the low-accuracy point cloud, and therefore fusion of two groups of data information is realized. The method includes steps: (1) setting up topological structure information of the low-accuracy point cloud, and the topological structure information comprises neighborhood point sets and unit normal vector of every sample point; (2) utilizing the high-accurate point cloud to carry out density clustering on the low-accuracy point cloud, and ensuring shift errors of every sample point of the low-accuracy point cloud according to a clustering result; and (3) utilizing the topological structure information of the low-accuracy point cloud and shift errors to ensure the shift vector of every sample point of the low-accuracy point cloud, and then carrying out shift on every sample point of the low-accuracy point cloud according to the shift vector to realize fusion. According to the fusion method, shift errors of the low-accuracy point cloud are eliminated, and fairing of small-amplitude noises can be realized at the same time.

Description

technical field [0001] The present invention belongs to the field of surface digital three-dimensional shape detection data processing. High-precision point cloud data is generally obtained through online contact detection of three-coordinate measuring machines or machine tools. Low-precision point clouds generally refer to three-dimensional points obtained by laser scanners or flexible joint arms. cloud data. Background technique [0002] With the increasing development of the manufacturing industry, the shape of the product is becoming more and more complex, and its development and design are facing many difficulties and challenges. In particular, the product shape modeling technology is facing more challenges. Complex curved surface parts represented by aviation blades and propellers have been widely used, and higher requirements have been put forward for the actual processing quality inspection, which has made great progress in the digital inspection of complex curved su...

AI Technical Summary

Problems solved by technology

This method is easy to implement, but the size of the above-mentioned drift vector cannot truly reflect the drift error between the low-precision point cloud and the high-precision point cloud, so that some areas of the low-precision point cloud have over-drift or under-drift, and the accuracy of the improvement is limited.

In fact, the drift error of each sample point of the low-precision point cloud is treated equally, which cannot truly reflect the difference in drift in different regions of the low-precision point cloud

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