Improved Euclidean clustering-based scattered workpiece point cloud segmentation method

Abstract

The invention provides an improved Euclidean clustering-based scattered workpiece point cloud segmentation method and relates to the field of point cloud segmentation. According to the method, a corresponding scene segmentation scheme is proposed in view of inherent disorder and randomness of scattered workpiece point clouds. The method comprises the specific steps of preprocessing the point clouds: removing background points by using an RANSAC method, and removing outliers by using an iterative radius filtering method. A parameter selection basis is provided for online segmentation by adopting an information registration method for offline template point clouds, thereby increasing the online segmentation speed; a thought of removing edge points firstly, then performing cluster segmentation and finally supplementing the edge points is proposed, so that the phenomenon of insufficient segmentation or over-segmentation in a clustering process is avoided; during the cluster segmentation, an adaptive neighborhood search radius-based clustering method is proposed, so that the segmentation speed is greatly increased; and surface features of workpieces are reserved in edge point supplementation, so that subsequent attitude locating accuracy can be improved.

Description

technical field [0001] The invention relates to the field of point cloud segmentation, in particular to a point cloud segmentation method for scattered workpieces based on improved European clustering. Background technique [0002] In recent years, with the improvement of the accuracy and cost reduction of 3D scanning equipment, researchers can quickly and accurately obtain the 3D point cloud information of the object surface. Compared with two-dimensional images, point clouds contain the depth information of objects, and have unique advantages and potentials in target recognition and positioning. Therefore, this technology has aroused widespread interest in the field of Random Bin Picking (RBP). focus on. Using 3D scanning equipment to obtain point clouds on the surface of scattered workpieces in the box, combined with point cloud processing algorithms to calculate the pose of a single workpiece, and guide industrial robots to grasp, the efficiency is higher, the speed is ...

AI Technical Summary

Problems solved by technology

The Euclidean clustering algorithm classifies points according to the Euclidean distance between points, and classifies points whose distance is smaller than the threshold as the current class, but this method needs to manually set the distance threshold between points. If the threshold is too large or too small, it will Lead to different degrees of under-segmentation and over-segmentation, affecting the effect of segmentation

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