基于改进欧氏聚类的散乱工件点云分割

田青华; 白瑞林; 李杜

doi:doi:10.3788/lop54.121503

激光与光电子学进展, 2017, 54 (12): 121503, 网络出版: 2017-12-11

基于改进欧氏聚类的散乱工件点云分割下载： 968次

Point Cloud Segmentation of Scattered Workpieces Based on Improved Euclidean Clustering

论文大纲

田青华 ^1,*白瑞林 ¹李杜 ²

作者单位

¹ 江南大学轻工过程先进控制教育部重点实验室, 江苏无锡 214122

² 无锡信捷电气股份有限公司, 江苏无锡 214072

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摘要

针对机器人随机箱体抓取过程中场景分割困难的问题, 提出一种基于改进欧氏聚类的散乱工件点云分割方法。采用直通滤波法和迭代半径滤波法进行预处理, 得到去除干扰点后的散乱工件点云; 通过基于法线夹角的边缘检测去除点云中的边缘点, 并使相互碰撞的工件在空间上产生分离; 采用改进的搜索半径自适应欧氏聚类进行点云分割, 得到多个工件点云子集, 基于距离约束将去除的边缘点补齐到点云子集之中, 从而完成点云分割。此外, 线下模板点云注册为分割参数的选取提供依据, 从而保证了分割结果的准确性, 提高了分割速度。结果表明：基于改进欧氏聚类的散乱工件点云分割方法能够准确地分割出感兴趣的工件, 分割时间约为696 ms, 满足了工业机器人抓取的实时性要求。

Abstract

Aiming at the difficulty of scene segmentation in the process of robotic random bin picking, a point cloud segmentation method based on the improved Euclidean clustering is proposed. The pass-through filter and the iterative radius filtering are used for the pretreatment to obtain the point cloud of scattered workpieces after removing the interference points. The edge points in the point cloud are removed by the edge detection based on normal angle, and the inter-collision workpieces are separated in space. The improved radius adaptive Euclidean clustering is adopted for the point cloud segmentation to obtain the point cloud subsets of many workpieces. The removed edge points will be put into the point cloud subsets based on the distance constraint, and thus the point cloud segmentation is completed. In addition, the offline template point cloud provides reference for the selection of segmentation parameters, which ensures the accuracy of segmentation results and improves the segmentation speed. The experimental results show that the proposed method can accurately segment the interested workpieces, and the segmentation time is about 696 ms. It is satisfied with the real-time requirement of industrial robot picking.

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田青华, 白瑞林, 李杜. 基于改进欧氏聚类的散乱工件点云分割[J]. 激光与光电子学进展, 2017, 54(12): 121503. Tian Qinghua, Bai Ruilin, Li Du. Point Cloud Segmentation of Scattered Workpieces Based on Improved Euclidean Clustering[J]. Laser & Optoelectronics Progress, 2017, 54(12): 121503.

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