吴飞,赵新灿,展鹏磊,关凌.自适应邻域选择的FPFH特征提取算法[J].计算机科学,2019,46(2):266-270
自适应邻域选择的FPFH特征提取算法
FPFH Feature Extraction Algorithm Based on Adaptive Neighborhood Selection
投稿时间:2018-01-24  修订日期:2018-05-24
DOI:
中文关键词:  FPFH,SAC-IA配准,点云密度,邻域半径
英文关键词:Fast point feature histograms,Sample consensus initial alignment,Point cloud density,Neighborhood radius
基金项目:本文受国家自然科学基金委员会-中国民航局民航联合研究基金(U1433106)资助
作者单位E-mail
吴飞 郑州大学信息工程学院 郑州450001  
赵新灿 郑州大学信息工程学院 郑州450001 iexczhao@zzu.edu.cn 
展鹏磊 郑州大学信息工程学院 郑州450001  
关凌 郑州大学信息工程学院 郑州450001  
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中文摘要:
      在使用点云FPFH(Fast Point Feature Histograms)特征进行三维物体识别或配准时,人为主观调整邻域半径计算FPFH特征描述符具有随意性、低效性,整个过程不能自动化完成。针对该问题,提出了自适应邻域选择的FPFH特征提取算法。首先,对多对点云估算点云密度;然后,计算多个邻域半径以提取FPFH特征用于SAC-IA配准,统计配准性能最优时的半径与点云密度值,使用三次样条插值拟合法求出函数表达式,形成自适应邻域选择的FPFH特征提取算法。实验结果表明,该算法根据点云密度自适应选择合适的邻域半径,提升了FPFH特征匹配的性能,同时 加快了运算速度,具有指导价值。
英文摘要:
      When using the FPFH feature of point cloud for 3D object recognition or registration,FPFH feature descriptor is arbitrarily and inefficiently calculated by subjectively adjusting the neighborhood radius,and the whole process can not be completed automatically.This paper proposed an adaptive neighborhood-selection FPFH point cloud feature extraction algorithm to solve this problem.Firstly,the point cloud densities of many pairs of point clouds were estimated.Secondly,the neighborhood radii were computed to extract the FPFH features for SAC-IA,and the radii and the densities were counted when the registration performance is the most optimal,and then the Cubic Spline Interpolation Fitting was used to fit the function expression of the radii and the densities to form the adaptive neighborhood-selection FPFH feature extraction algorithm.The experimental results show that this algorithm can adaptively choose the appropriate neighborhood radius according to the density of point cloud,improves the FPFH feature matching perfor-mance,and improves the computing speed at the same time,indicating that the proposed algorithm is of important guiding significance.
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