Improved algorithm for point cloud registration based on fast point feature histograms
Improved algorithm for point cloud registration based on fast point feature histograms
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DOI：
10.1117/1.jrs.10.045024
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发表时间：
2016-12-30
影响因子：
1.7
通讯作者：
Yao, Yifei
中科院分区：
文献类型：
--
作者：
Li, Peng;Wang, Jian;Yao, Yifei
点云配准是三维点云数据处理中非常重要的一环，其配准结果直接影响到三维物体重建等应用。目前，点云配准的方法很多，但这些方法不能同时解决效率和精度的问题。提出了一种基于快速点特征直方图的点云配准方法，该方法首先根据快速点特征直方图从点云数据集中提取特征点，然后在给定的约束条件下找到4个点对点对应关系，这些点对点之间的特征、距离和位置关系都是给定的。然后，在最初的四个点对的基础上添加额外的点对，直到点对的数量满足点云配准的要求。最后，根据点对的对应关系计算出刚体变换矩阵。实验结果表明，该方法在大多数类型的数据集上都具有较高的配准效率和精度。(C)2016年光学仪器工程师学会(SPIE)
Point cloud registration is very important in three-dimensional (3-D) point cloud data processing as its results directly affect 3-D object reconstruction and other applications. Currently, there are many methods for point cloud registration, but these methods are not able to simultaneously solve the problem of both efficiency and precision. We propose a method of point cloud registration based on fast point feature histogram (FPFH), in which feature points are first extracted from the point cloud dataset according to FPFH and four point-to-point correspondences are found within some given constraints regarding their features, distances, and location relationships. Then, additional point pairs are added on the basis of the initial four point pairs until the number of point pairs satisfies the requirements for point cloud registration. Finally, a rigid transformation matrix is calculated from the correspondence of the point pairs. The results show that there is both a high efficiency and precision in most types of datasets when using this method for point cloud registration. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)