改进的基于FPFH特征配准点云的方法

ICP(Iterative Closest Point)算法是点云配准中最常用的算法，而点云的FPFH(Fast Point Feature Histograms)特征可在点云配准中为其提供初始匹配信息。针对该方法的初始匹配中距离测度等问题，提出一种改进的基于FPFH特征配准点云的方法。点云配准时首先计算2个点云的点的FPFH特征之间的巴氏距离，以k-d树检索巴氏距离最小的对应点，然后利用奇异值分解计算初始转换矩阵，进行ICP算法精细匹配，求得最终变换矩阵。实验结果表明，改进的基于FPFH特征配准点云的方法能为ICP算法提供良好的初始变换矩阵，在同等迭代次数下该方法具有更高的精度。

Improved Method of Point Cloud Registration Based on FPFH Feature

ICP algorithm is the most commonly used algorithm in point cloud registration, and the FPFH feature can provide initial matching information to register point cloud. An improved method based on FPFH feature registration of point cloud is proposed. Firstly, the Bhattacharyya distance between the FPFH features of two point clouds is calculated. The k-d tree is used to retrieve the corresponding points with the smallest Bhattacharyya distance. Then, the initial transformation matrix is calculated, the ICP algorithm gets the final transformation matrix. Experiment shows that the method has higher accuracy under the same iterations.