Target-contour three-dimensional information extraction method based on edge curvature angle

Abstract

The invention discloses a target-contour three-dimensional information extraction method based on an edge curvature angle. The method comprises the following steps of (1) using a machine vision measurement technology to acquire three-dimensional point cloud data of a target object contour; (2) carrying out filtering denoising processing on the three-dimensional point cloud data; (3) ordering the denoised point cloud data through an annular ordering algorithm so that the point cloud data is arranged annularly according to an anti-clockwise direction, wherein adjacent points are stored at adjacent positions of a linear list at this time; (4) according to an annular ordering result, carrying out cluster segmentation on the point cloud data; (5) based on an edge curvature angle, rapidly dividing the point cloud data into linear points or curve points; and (6) based on point classification, carrying out segmentation fitting on the point cloud data and recovering three-dimensional information of the target contour. In the invention, the three-dimensional information of an object with a regular shape can be accurately extracted in real time.

Description

technical field [0001] The invention relates to the field of machine vision, in particular to measurement technology, in particular to a method for extracting three-dimensional information of a target outline based on an edge curvature angle. Background technique [0002] After using the binocular vision technology to obtain the 3D point cloud data of the edge contour of the target object, in order to extract the 3D feature information such as shape, size and position, it is necessary to further process these point cloud data. At present, the commonly used processing method is to manually select the key points in the point cloud data, and then calculate the actual size of the target object through the geometric relationship between them. The measurement accuracy of this method is easily affected by human factors, and it is difficult to realize automatic measurement. . In addition, the minimum circumscribed rectangle method is also used for size measurement. This method can ...

AI Technical Summary

Problems solved by technology

At present, the commonly used processing method is to manually select the key points in the point cloud data, and then calculate the actual size of the target object through the geometric relationship between them. The measurement accuracy of this method is easily affected by human factors, and it is difficult to realize automatic measurement.

In addition, the minimum circumscribed rectangle method is also used for size measurement. This method can realize automatic measurement and has better real-time performance. It has a better measurement effect on rectangular targets, but it can only estimate the size of non-rectangular workpieces.

However, the traditional method of using contour data fitting to extract size information does not consider the real-time performance of the algorithm, and is not suitable for occasions that require high real-time performance.

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