Gear scanning algorithm for template matching and point cloud comparison

Abstract

The invention discloses a gear scanning algorithm for template matching and point cloud comparison. The method comprises a line scanning algorithm, a splicing algorithm, a model matching algorithm anda model comparison algorithm, and the main working logic is that a line laser scans the surface contour of a workpiece through the movement of a linear module, and each section contour of the workpiece is obtained through the analysis and calculation of the line scanning algorithm; splicing the obtained section contours in real time through a splicing algorithm to obtain a three-dimensional pointcloud of an actual workpiece; aligning a theoretical three-dimensional model and the actual three-dimensional point cloud as a reference through a model matching algorithm; comparing the two three-dimensional models subjected to reference alignment to obtain the defect information of each gear. According to the method, a rapid algorithm for scanning the gears and determining the defect positionsis realized through the combination of various algorithms and hardware data, so that the speed and accuracy of determining the defect positions of the gears are greatly improved, and the subsequent determination of the positioning parameters for repairing the gears is facilitated.

Description

technical field [0001] The invention specifically relates to a scanning algorithm, in particular to a gear scanning algorithm for template matching and point cloud comparison. Background technique [0002] In reverse engineering, the point data set on the surface of the product obtained by measuring instruments is also called point cloud. Usually, the number of points obtained by using a three-dimensional coordinate measuring machine is relatively small, and the distance between points is relatively large, which is called sparse point cloud. ; and the point cloud obtained by using a 3D laser scanner or a photo scanner has a relatively large number of points and is relatively dense, which is called a dense point cloud. The point cloud obtained according to the principle of laser measurement, including three-dimensional coordinates and laser reflection intensity. [0003] After the grinder is used for a long time, the gears of the grinder are prone to broken teeth and missing...

AI Technical Summary

Problems solved by technology

[0003] After the grinder is used for a long time, the gears of the grinder are prone to broken teeth and missing teeth. For the problem of broken teeth and missing teeth, the traditional method is to use manual welding to repair the gears. Because of the pure manual welding repair, the repair is good. Damage often depends on the technical level of workers, human factors are large, and the quality of repair cannot be guaranteed; repair requires a lot of labor, and the working environment is harsh; because manual repair cannot guarantee the consistency of dimensions, repair is inevitable, resulting in loss of manpower and material resources. Many welding robots are used for repairs, but welding robots need data information on the position of gear defects. At present, a lot of defect position information is analyzed by means of image forming analysis or scanning, but the general defect position positioning is not accurate, lack of comparative control, and the acquisition of defect The location algorithm is slow, so for this problem, we need a fast intelligent algorithm for gear defect location