非结构环境下一种改进的区域生长点云分割方法  

An Improved Segmentation Method of Regional Growth Point Cloud in Unstructured Environment

作　　者：刘玮 李岩[1] 贾科 刘克平[1] LIU Wei;LI Yan;JIA Ke;LIU Ke-ping(School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China;Changchun FAW International Logistics Co. Ltd, Changchun 130000, China)

机构地区：[1]长春工业大学电气与电子工程学院,长春130012 [2]长春一汽国际物流有限公司,长春130000

出　　处：《科学技术与工程》2021年第27期11650-11655,共6页Science Technology and Engineering

基　　金：吉林省发改委项目(2020C018-1)。

摘　　要：针对非结构环境下目标零件识别过程中零件相互堆叠造成分割困难的问题,提出了一种改进的区域生长点云分割算法。首先,采用直通和统计滤波器对获取的点云进行预处理,得到去除冗余数据后的点云;其次,使用八叉树建立点云之间的拓扑关系,提高后续点云分割的效率;最后,选择基于局部径向基函数的曲率计算方法,通过计算选取曲率最小点为初始种子点,并设定空间阈值范围进行区域生长。实验结果表明:该方法能有效解决非结构环境下零件堆叠导致的分割困难问题,分割准确度高且效率满足工业实时需求。An improved region growing point cloud segmentation algorithm was proposed to solve the problem of difficult segmentation in the process of target parts recognition in unstructured environment due to the stacking of parts.Firstly,the point cloud obtained was preprocessed by pass-through and statistical filter to obtain the point cloud after removing redundant data.Secondly,Octree was used to establish the topological relationship between point clouds to improve the efficiency of subsequent point cloud segmentation.Finally,the curvature calculation method based on local radial basis function was selected.The minimum point of curvature was selected as the initial seed point through calculation,and the range of spatial threshold was set for regional growth.Experimental results show that the proposed method can effectively solve the difficult segmentation problem caused by the stacking of parts in the unstructured environment,and the segmentation accuracy is high and the efficiency can meet the real-time needs of industry.

关 键 词：目标零件识别 点云分割 区域生长 点云预处理 八叉树 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]