迭代插值FPFH特征点云配准算法

针对三维激光扫描点云数据的配准问题,提出了一种基于FPFH特征的迭代插值配准新方法。配准过程中考虑到点云数据获取时,受扫描仪分辨率影响,点云局部或整体密度偏小,两次测量点云数据的相同位置不存在完全相同的点,以致对应点之间存在误差。为减小误差对配准精度影响,引入迭代插值方法,增加点云整体密度。配准过程通过计算关键点处FPFH特征寻找对应相关关系求得粗配准旋转平移矩阵,再使用ICP算法进行点云的精确配准。实验结果表明,改进的配准方法简单、稳定可靠、计算速度快且计算复杂度小,对实现点云配准具有实用价值。

Iterative Interpolation Point cloud Registration based on FPFH

To solve the registration of 3D laser scanning point cloud data, a new method of iterative interpolation registration based on FPFH was proposed. Affected by the scanning resolution,density of partial or overall of obtained point cloud data was small such that difference point clouds have different point on the same location. So that errors exist between corresponding points. In order to reduce this influence on the accuracy of registration, iterated interpolation method was proposed, increasing the overall density of point cloud. The FPFH features of key points were used to find the corresponding relationship between two point cloud datas,then coarse registration rotation and translation matrix were gotten. Last ICP algorithm was used for precise registration of point clouds.The experimental results show that the improved registration algorithm is simple, stable and reliable, have fast calculation speed and small computational complexity, and has practical significance to the realization of point cloud registration.#$NLKeywords: point cloud registration, iterative interpolation, key points, FPFH, ICP