基于核密度估计的散乱点云边界特征提取  

Boundary Feature Abstraction of Unorganized Points Based on Kernel Density Estimation

作　　者：孙殿柱[1] 刘华东[1] 史阳[1] 李延瑞[1] 

机构地区：[1]山东理工大学机械工程学院,淄博255091

出　　处：《农业机械学报》2013年第12期275-279,268,共6页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家自然科学基金资助项目(51075247);山东省自然科学基金资助项目(ZR2010EM008)

摘　　要：为获得逆向工程中复杂散乱点云的边界特征,提出了一种基于k邻域点集核密度估计的边界特征识别与提取算法,通过R*树索引结构和动态扩展空心球算法实现样点k邻域点集的快速查询,将查询区域半径作为带宽对点集进行核密度估计,由核密度估计获得反映点集分布的模式点,依据模式点到样点的距离与带宽的比值判别边界点特征。实验结果表明,该算法能够快速、准确提取逆向工程中均匀及非均匀分布的散乱点云的边界特征。In order to obtain the boundary feature of unorganized points, a method of boundary feature recognition and abstraction was proposed based on the kernel density estimation on k-neighborhood of every sample point, k-neighborhood of a sample point could be acquired quickly based on R＊ -tree index, and the radius of query area viewed as bandwidth was used to kernel density estimation on the point set consisted of the sample point and its k-neighborhood. In this way, the mode points reflected the distribution of point sets could be obtained. According to the ratio of distance between mode points and sample points to the bandwidh of kernel density estimation, the sample points located on boundary could be recognited and abstracted. The experimental results show that the algorithm can obtain the boundary feature of the unorganized points in uniform or nonuniform distribution exactly and rapidly.

关 键 词：散乱点云 边界特征 R^＊树 k邻域查询 核密度估计 

分 类 号：TP391.72[自动化与计算机技术—计算机应用技术]