摘要
为了有效获取散乱点云中的尖锐特征点和边界特征点,提出一种利用多判据融合的特征点提取算法。首先利用一种改进的k-d tree构建点云拓扑,搜索样点的K局部邻域;然后利用法向夹角判定准则、核密度判定准则、场力和判定准则分别求取各个样点局部邻域的三个特征参数,最后通过加权计算特征参数得到每个样点的特征值与全局判定阈值,特征值比阈值大的点即为特征点。实验证明,该算法能有效地获取散乱点云中边沿特征点与尖锐特征点。
This paper proposed an algorithm of extracting feature points based on multiple criterions,which aimed to extract boundary feature points and sharp feature points efficiently.Firstly,the algorithm built the point cloud topological structure based on a modified k-d tree approach to search for the K-nearest neighborhood of the sample point.Then,according to each K-nearest neighborhood points,it calculated three feature parameters based on vector angle criteria,kernel density criteria and field power criteria.Finally,according to the three parameters,it obtained feature discriminant parameters and the global fixed threshold.It recognized a point as the feature point when its value of discriminant parameter was bigger than the threshold.The experimental results show that the proposed algorithm can extract boundary points and sharp feature points effectively.
作者
王庆华
黄茹楠
闫晓庚
程拓
Wang Qinghua;Huang Runan;Yan Xiaogeng;Cheng Tuo(College of Electrical Engineering, Yanshan University, Qinhuangdao Hebei 066000, China;Electrical Intelligence Business Department, China National Heavy Machinery Research Institute Co. Ltd., Xi’an 710023, China)
出处
《计算机应用研究》
CSCD
北大核心
2019年第5期1585-1588,共4页
Application Research of Computers
基金
国家自然科学基金资助项目(61472341)
关键词
散乱点云
特征提取
均值漂移
法相夹角
场力和
scattered point cloud
feature point extraction
mean-shift
vector angle
sum of the field power
