基于下采样优化的点云配准方法  

Point Cloud Registration Method Based on Down Sampling Optimization

作　　者：王明[1] 邓志良 严飞[1,2] 刘佳 Wang Ming;Deng Zhiliang;Yan Fei;Liu Jia(Automation College Nanjing University of Information Science and Technology,Nanjing 210044,Jiangsu,China;Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology,Nanjing 210044,Jiangsu,China)

机构地区：[1]南京信息工程大学自动化学院,江苏南京210044 [2]江苏省大气环境与装备技术协同创新中心,江苏南京210044

出　　处：《应用激光》2024年第5期201-207,共7页Applied Laser

基　　金：江苏省产业前瞻与关键核心技术重点项目(BE2020006-2)。

摘　　要：针对点云数据量庞大导致配准效率低下以及部分空间结构复杂易产生误匹配等问题,提出一种基于精简点云优化粗配准的点云配准算法。在体素下采样的基础上根据邻域点到中心点法线的角度均值差提取关键点;采用快速点特征直方图(FPFH)作为特征描述子;并在对应关系查找方面,根据邻近匹配对之间向量夹角的相似性结合随机抽样一致性(RANSAC)算法进行筛选优化,精确对应关系,完成粗配准;最后通过ICP算法实现精配准。实验结果表明,在点云表面空间变化差异较大的地方,所提算法能够有效地提取关键点,良好的对应关系为后续精配准提供了较好的初始位姿,有效地缩短了点云配准时间。This paper addresses the challenges of inefficient registration in point cloud data processing due to volume and complexity of spatial structures by introducing an optimized point cloud registration algorithm.The algorithm integrates a refined point cloud with an initial rough registration step.Key points are identified through voxel sampling,leveraging the variance in mean angles between neighborhood points and the center point normal line.The Fast Point Feature Histogram(FPFH)serves as the feature descriptor.In the search of correspondence relationship,according to the similarity of vector angle between adjacent matching pairs and Random Sampling Consistency(RANSAC)algorithm,filtering optimization is carried out to accurately correspond to the relationship and complete rough registration.Finally,the ICP algorithm is used to achieve accurate registration.The results show that the proposed algorithm effectively captures key points in regions of significant spatial variation,providing an advantageous initial position for precise registration and significantly reducing overall point cloud registration time.

关 键 词：机器视觉 点云配准 关键点提取 特征匹配 

分 类 号：TP391436[自动化与计算机技术—计算机应用技术]