Scattered workpiece recognition and positioning method based on point cloud processing

Abstract

The invention discloses a scattered workpiece recognition and positioning method based on point cloud processing, and the method is used for solving a problem of posture estimation of scattered workpeics in a random box grabbing process. The method comprises two parts: offline template library building and online feature registration. A template point cloud data set and a scene point cloud are obtained through a 3D point cloud obtaining system. The feature information, extracted in an offline state, of a template point cloud can be used for the preprocessing, segmentation and registration of the scene point cloud, thereby improving the operation speed of an algorithm. The point cloud registration is divided into two stages: initial registration and precise registration. A feature descriptor which integrates the geometrical characteristics and statistical characteristics is proposed at the stage of initial registration, thereby achieving the uniqueness description of the features of a key point. Points which are the most similar to the feature description of feature points are searched from a template library as corresponding points, thereby obtaining a corresponding point set, andachieving the calculation of an initial conversion matrix. At the stage of precise registration, the geometrical constraints are added for achieving the selection of the corresponding points, therebyreducing the number of iteration times of the precise registration, and reducing the probability that the algorithm falls into the local optimum.

Description

technical field [0001] The invention relates to target recognition and positioning in the field of machine vision, in particular to a method for recognizing and locating scattered workpieces based on point cloud processing, which can realize pose estimation of workpieces with arbitrary postures. Background technique [0002] In order for the robot to locate and grasp workpieces in the correct orientation from randomly placed and unsorted bins, pose estimation of the target workpiece is required. In recent years, 3D scanning devices (such as 3D laser scanners, stereo vision systems, etc.) Obtaining the rigid transformation between the two, so as to realize the recognition and positioning of the workpiece has become a research hotspot in the field of machine vision. Using the 3D point cloud processing algorithm to calculate the pose of a single workpiece, guide the robot to carry out the grasping operation, with high efficiency and fast speed, and can improve industrial produ...

AI Technical Summary

Problems solved by technology

[0004] According to the geometric characteristics of different objects, selecting the corresponding registration algorithm can realize the accurate and fast positioning of the workpiece, but there is still no general target recognition and positioning algorithm that can be applied to all objects, and it needs to be continuously adjusted according to different objects. Program

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