基于改进FPFH的栅极件点云匹配方法  

Registration method for grid point cloud based on improved FPFH

作　　者：张汝枭 方宇 杨皓 杭观荣 陶翰中 宁业衍 ZHANG Ruxiao;FANG Yu;YANG Hao;HANG Guanrong;TAO Hanzhong;NING Yeyan(School of Mechanical and Automotive Engineering,Shanghai University of Engineering Science,Shanghai 201620,China;Shanghai Institute of Space Propulsion,Shanghai 201112,China)

机构地区：[1]上海工程技术大学机械与汽车工程学院,上海201620 [2]上海空间推进研究所,上海201112

出　　处：《上海工程技术大学学报》2023年第4期414-419,共6页Journal of Shanghai University of Engineering Science

基　　金：上海市松江区科技攻关项目资助(20SJKJGG08C)。

摘　　要：针对栅极件点云的匹配问题,提出一种改进的快速点特征直方图(Fast Point Feature Histogram,FPFH)描述子.在FPFH基础上增加邻域密度作为点特征的描述,并在使用k维树(k-dimensional tree,k-d tree)算法搜索对应点时将巴氏距离作为指标.仿真过程中,将该方法与传统FPFH的匹配效果进行比较.搭建试验装置采集栅极件表面点云数据,并利用改进的FPFH算法和经典的迭代最近点算法(Iterative Closest Point,ICP)进行匹配.试验结果表明,匹配后的点云尺寸精度在1μm级别,栅孔误差范围在±(20~40)μm内,理论上满足了栅极件测量精度要求.An improved fast point feature histogram(FPFH)was proposed for the initial registration of grid point cloud.The neighborhood density was added as a point feature description on the basis of FPFH.When searching the corresponding points with k-dimensional tree(k-d tree),the Bhattacharyya distance was used as the index.This improved method was compared with FPFH in the simulation.Then,the test device was built to collect the grid’s surface point cloud data.The data were registered with improved FPFH method and iterative closest point(ICP).Test results show that the precision level of the matched point cloud is 1μm and the error range of grid hole stays in±(20~40)μm,which theoretically satisfy the precision requirement of the grid.

关 键 词：栅极件 自动化测量 点云匹配 快速点特征直方图 邻域密度 巴氏距离 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]