Complex component point cloud splicing method and system based on feature fusion

Abstract

The invention discloses a complex component point cloud splicing method and system based on feature fusion, and belongs to the field of computer vision, and the method comprises the steps: collecting the local three-dimensional point cloud of a component, and carrying out the positioning, wherein the adjacent local three-dimensional point clouds are partially overlapped; converting the local three-dimensional point clouds to a world coordinate system according to a positioning result, and extracting key points after uniform downsampling to obtain corresponding key point clouds; inputting the key point clouds into a feature fusion matching network to obtain a matching corresponding relationship between any two adjacent key point clouds, wherein the feature fusion matching network takes two adjacent point clouds as input and is used for extracting multi-scale features or multiple features of points in the point clouds and fusing the multi-scale features or the multiple features into feature descriptors of corresponding points so as to calculate a matching corresponding relation of the adjacent point clouds; and carrying out point cloud registration by using a matching corresponding relation of adjacent key point clouds, and splicing the key point clouds into an integral three-dimensional point cloud model based on a point cloud registration result. According to the invention, the precision of point cloud splicing of complex components can be improved.

Description

technical field [0001] The invention belongs to the field of computer vision, and more specifically relates to a method and system for splicing complex constructed point clouds based on feature fusion. Background technique [0002] Large and complex components, including aircraft wings, ship blades, wind blades, high-speed rail bodies, etc., are widely used in many major technical fields such as aerospace, energy and transportation. In order to achieve high-precision machining of the surface of large and complex components, it is necessary to develop an accurate and reliable three-dimensional measurement system to evaluate the surface accuracy of its various parts. Just like using a ruler to measure the scale of an object, if you want to get a sufficiently reliable measurement result, the accuracy of the ruler needs to be high enough. The same is true for the measurement of the surface accuracy of complex components. In order to obtain reliable measurement results, three-dim...

AI Technical Summary

Problems solved by technology

The six-degree-of-freedom transformation model estimated by traditional rigid body registration algorithms such as ICP and RANSAC cannot fit more complex non-rigid body transformations, so the relative pose transformation between local point clouds cannot be accurately estimated, resulting in the accuracy of point cloud stitching Decline

[0008] In summary, for the existing point cloud stitching method, it is not reliable in the context of the actual application of 3D measurement of large and complex components, and cannot meet the specific application requirements of high-precision 3D measurement

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