A target object size measurement method of sparse point cloud

Abstract

The invention relates to a method for measuring the size of a target object of sparse point cloud. The method comprises the following technical steps of: converting a point cloud coordinate system into an image pixel coordinate system; Projecting the point cloud data on an XY plane to obtain a two-dimensional point cloud; Carrying out edge detection on the matched image by using a Canny operator;De-noising and refining the edge contour line by adopting an image morphology method; Carrying out traversal search on the point cloud image, and carrying out pixel point coordinate matching on the point cloud image and the binary contour image; And extracting the point cloud coordinates which are matched and matched to form a new point cloud, and carrying out linear fitting and size measurement on the new point cloud. According to the invention, through overlapping the point cloud image and the contour image after stereo matching, the point cloud on the contour line is extracted, the linear fitting algorithm is used to carry out linear fitting on the extracted new point cloud coordinate, and the size measurement is further carried out.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a method for measuring the size of an object with sparse point clouds. Background technique [0002] The point cloud data obtained by the computer vision system has two characteristics: sparse and scattered point cloud; there are noise points with mismatching. In 3D reconstruction technology, there are generally two methods for point cloud contour extraction of the target object: (1) constructing a triangular mesh from the surface model reconstructed from the point cloud, and generating contour lines based on the triangular mesh; (2) directly by Space linear fitting and surface fitting of point cloud requires slice processing of point cloud data. The method of extracting contours by constructing triangular meshes is very computationally intensive and has high requirements for point clouds. It is best to use dense point clouds. These reconstructed models have better effec...

AI Technical Summary

Problems solved by technology

The method of extracting contours by constructing triangular meshes is very computationally intensive and has high requirements for point clouds. It is best to use dense point clouds. These reconstructed models have better effects, and the edges of concave edges There will be narrow and long triangles, and the extraction of edge points is prone to large errors; while the direct extraction method needs to segment the point cloud data, and needs to calculate the curvature of each surface, and the segmentation threshold is not easy to determine, and the amount of calculation is also very large. Big

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