摘要： 在快速点特征直方图（FPFH）的特征计算过程中，需要人工多次选择邻域半径，计算过程复杂且效率较低。针对该问题，提出基于弧长密度的自动邻域半径鉴别FPFH特征提取算法。给出点云弧长密度的计算方法，依据弧长密度估算多对点云的邻域半径，以提取FPFH特征并完成采样一致性初始配准，确定配准性能最优的半径与弧长密度值。在此基础上，使用最小二乘法拟合邻域半径与弧长密度之间的函数表达式，并与FPFH特征提取算法结合得到自动邻域半径鉴别FPFH特征提取算法。实验结果表明，该算法可根据点云弧长密度自动鉴别出合适的邻域半径，运算速度较快。

关键词: 点云密度, 邻域半径, 快速点特征直方图, 采样一致性初始配准, 最小二乘法

Abstract: The computation of Fast Point Feature Histogram (FPFH) features requires human intervention to choose the neighborhood radius,which adds to the complexity and decreases the efficiency of computation.To address the problem,an automatic neighborhood radius identification algorithm for FPFH feature extraction based on arc length density is proposed.The calculation method of point cloud arc length density is given.The neighborhood radius of multiple pairs of point clouds is estimated according to the arc length density to extract FPFH features and complete Sample Consensus Initial Aligment(SAC-IA).The optimal values of the radius and arc length density are determined.On this basis,the least square method is used to fit the function expression between the neighborhood radius and the arc length density,and combined with the FPFH feature extraction algorithm to form an automatic neighborhood radius identification FPFH feature extraction algorithm.The experimental results show that the algorithm can automatically identify the appropriate neighborhood radius according to the point cloud arc length density,and the computation speed is fast.

Key words: point cloud density, neighborhood radius, Fast Point Feature Histograms(FPFH), Sample Consensus Initial Alignment(SAC-IA), least square method

中图分类号: 

• TP391.7