Method, device, equipment and medium for generating grid data structure from disordered point cloud

Abstract

A method for generating a 5D texture grid data structure from a 3D disordered point cloud, comprising: obtaining a disordered point cloud of a workpiece to be scanned; obtaining a vertex S mn , and establish each point P in the disordered point cloud i with vertex S mn There is a one-to-one correspondence between them; sort the point cloud data, and obtain each point P i Sort by row and sort by column; traverse point cloud data, for any point P i , find P according to the index rules in sorting by row and sorting by column i Neighboring point P j ,P k ,P q ;According to the index relationship, get P i ,P j ,P k ,P q Corresponding vertex S mn , S m,n+1 .S m+1,n , S m+1,n+1 ; Add a quadrangular facet, and set S mn , S m,n+1 .S m+1,n , S m+1,n+1 Add to the vertex position of the quadrangular face and generate a half-sided quadrangular mesh, traverse P i or vertex S mn , get all P i or vertex S mn Corresponding half-sided quadrangular grid, output 5D texture grid data structure. The method is used to grid the unordered point cloud, and the unordered point cloud data is represented based on the data structure of the texture grid, which has a topological structure and can effectively represent the omnidirectional information of the three-dimensional object.

Description

technical field [0001] The invention belongs to the technical fields of computer graphics and computer vision, and in particular relates to a data gridding method and device for disordered point clouds, computer equipment and storage media. Background technique [0002] In the fields of industrial manufacturing and logistics, unordered sorting tasks such as loading and unloading of machine tools, sorting of parts, and sorting of goods are labor-intensive, low in sorting efficiency, and difficult to recruit for traditional manual operations. Robot disorder sorting technology is increasingly replacing manual labor, and has become an important means for related industries to reduce costs, increase efficiency, and upgrade intelligently. [0003] The random sorting of robots relies on visual recognition methods to locate the objects to be sorted, and then guides the end of the robot to complete the grabbing and placing operations of the objects. Generally, 2D images, 3D point cl...

AI Technical Summary

Problems solved by technology

[0004] At present, traditional 2D vision or 3D visual recognition methods cannot solve the problem of universal and accurate recognition of massive disordered posture workpieces, and it is difficult to meet the disordered sorting needs of e-commerce logistics

When the workpieces to be scanned have a large attitude inclination and are stacked in disorder, such as identifying instant noodles in barrels placed on the side of the turnover box, the traditional 2D method needs to make multiple templates with different viewing angles for matching and positioning. In this way, when facing a large number of workpieces, establish The workload of the template is too large; when the workpieces to be inspected are closely placed, it is difficult for the 3D vision method to segment different objects in the scene, and it is difficult to adapt to the workpieces to be inspected with small changes in object shape, such as identifying closely arranged mobile phone packages in a turnover box box

Only by identifying the complete shape of the workpiece (including the side and bottom surface), can the workpiece be accurately stacked to avoid damage to the workpiece when grabbing precious workpieces, while traditional 2D / 3D / deep learning methods can only be based on local information on the upper surface of the workpiece For identification, it is difficult to judge the complete shape of the workpiece to be measured, and it is difficult to adapt to accurate stacking

[0005] The above problems are mainly due to the fact that the existing visual recognition methods are limited to the data structure representing three-dimensional objects. Thanks to the matrix form of 2D images, the image processing methods in 2D vision are very mature, but it is difficult for 2D images to represent the three-dimensional objects. All-round information; In 3D visual processing, most of the existing 3D image processing methods are based on disordered point clouds, which is difficult to achieve ordered neighborhood access similar to 2D, so that 2D texture features cannot be used; 3D images based on disordered point clouds Processing method, since the disordered point cloud can only express the upper surface information of the workpiece, it cannot express the bottom surface and side surface, nor can it realize the expression of the complete shape

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