针对传统配准法不能很好解决大角度变换点云的配准这一问题,提出一种基于精确对应特征点对及其K 邻域点云的配准方法.首先分别计算两组点云的FPFH 值,根据特征值建立点云间的对应关系;然后通过RANSAC 滤除其中错误的匹配点对,得到相对精确的特征点对集合;之后通过KD-tree搜索的方式分别找出特征点对R 半径邻域内的点,应用ICP 算法得到两部分点云的最优收敛;最后将计算得到的相对位置关系应用到原始点云上得到配准结果.通过对斯坦福大学点云库中Dragon、Happy Buddha 模型以及Kinect采集的石膏像数据进行配准和比较,实验表明该方法能够有效解决大角度变换点云的配准问题,是一种具有高精度和高鲁棒性的三维点云配准方法.%To the problem of traditional registration algorithm are difficult to get the desired effect in large-angle pointcloud registration. We proposed a registration method based on exact correspondence feature point pairs and its K-neighbour pointclouds. Firstly, calculate the FPFH of the pointclouds separately, establishing correspondences between point clouds According to the eigenvalues; Then remove the erroneous matching point pairs by RANSAC, and obtain a relatively accurate set of feature point pairs; Moreover, using KD-tree search get the R-Rad region of the feature point pairs respectively, and applying ICP to obtain the optimal convergence of pointclouds. Finally, applying the ICP relative position relationship to the original pointclouds to get the final registration result. Through registration testing and comparison of the Stanford Dragon, Happy Buddha pointcloud models, and Gypsum data scanned by Kinect, The experiment shows that this method can effectively solve the registration problem of pointclouds with large angle transformation, its a 3D pointclouds registration method with high accuracy and robustness.
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