Three-dimensional identification and positioning method for sheet metal parts based on PCL point cloud library

Abstract

The invention discloses a three-dimensional identification and positioning method for sheet metal parts based on a PCL point cloud library. The three-dimensional identification and positioning methodcomprises the steps: obtaining a three-dimensional scene point cloud of an operation platform of a whole sheet metal part, carrying out the segmentation, and forming a point cloud cluster of each sheet metal part; calculating a local feature descriptor and a global feature descriptor of each point cloud cluster; performing feature-level fusion on the local feature descriptor and the global featuredescriptor of each point cloud cluster to obtain a fusion feature vector; inputting the fusion feature vector into a pre-trained SVM classifier for classification; solving an initial registration transformation matrix from point cloud clustering to point cloud model coarse registration; and determining an accurate transformation matrix conforming to the preset registration precision from the point cloud clustering to the point cloud model, and determining a pose information result of each sheet metal part according to the accurate transformation matrix. Through the technical scheme of the invention, the inaccuracy of two-dimensional image recognition is avoided, and the recognition efficiency and the positioning precision are greatly improved.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a three-dimensional recognition and positioning method for sheet metal parts based on a PCL point cloud library. Background technique [0002] At present, there have been many research results on image-based object recognition, but the imaging process of two-dimensional images is mapped from three-dimensional space to two-dimensional space. This process will lose a lot of information, and a fact that cannot be ignored is that the best A computer vision system should be oriented to a three-dimensional world. With the improvement of automation, the detection, grasping and processing of industrial parts have higher and higher requirements for computer vision systems, so object recognition based on two-dimensional images cannot meet the requirements. The acquisition of 3D point cloud data is very fast. At the same time, because the acquisition of 3D point cloud data is not a...

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Problems solved by technology

[0002] At present, there have been many research results on image-based object recognition, but the imaging process of two-dimensional images is mapped from three-dimensional space to two-dimensional space. This process will lose a lot of information, and a fact that cannot be ignored is that the best A com

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