结合NARF特征的改进型3D-NDT多视点云配准

提出了一种精确有效的多视图配准算法。首先，使用NARF算法对每幅点云进行关键点检测，并以NARF关键点为原点建立局部坐标系，估算FPFH描述符；其后使用基于RANSAC的对应估计和对应关系去除算法剔除错误对应关系，确定三维特征匹配点对，并求解出变换矩阵，完成初始配准。然后，使用3D-NDT算法体素化点云，并使用概率分布函数对点云精细配准。最后，使用逐步匹配法对一系列点云进行配准，使其全部配准到统一坐标系中。实验结果证明，该算法能精确的对由KinectV2.0获取的同一场景不同角度的多幅点云图像进行配准，且其配准精度较高。 

Multi-view Point Cloud Registration Based on Improved 3D-NDT Combining The Feature of NARF

An accurate and efficient algorithm of multi-view registration algorithm is proposed in this paper. First，corresponding feature points in each 3D images are found by using the NARF algorithm, and the FPFH feature of the keypoints is described , in a local coordinate system with origin in the keypoint position of the corresponding range image,and then false matches are eliminated by Correspondence Estimation and Correspondence Rejector Sample Consensus based on RANSAC. According to the corresponding relation, the matching feature points are identified, the transformation matrix is calculated, and the initial registration of point clouds is completed. Then, the point clouds are divided into three-dimensional voxel grids and registered precisely by the probability distribution function based on initial registration. Furthermore, incrementally register a series of point clouds two by two in order to transform all the clouds in the first cloud’s frame. The experiment results indicate that the method proposed in this paper can accurate register the point clouds on different viewpoints acquired by Kinect V2.0,and the efficiency of point cloud registration greatly.