针对点云配准时间长、收敛缓慢、对应点匹配易错等缺点，提出一种基于内部形态描述子（ISS）特征点结合改进迭代最近点(ICP)的点云配准算法。首先采用ISS算法进行点云特征提取，并以快速点特征直方图进行特征描述，然后通过采样一致性算法完成点云的初始配准，使两片不同角度点云获得一个相对较好的初始位姿，最后通过k维树近邻搜索法加速对应点对的查找，以提高点云ICP精细配准效率。实验结果表明，与传统配准算法相比，该算法配准精度高，而且执行速度快。

Aiming at the problems of long reconstruction time，slow convergence and error matching corresponding points for the point cloud registration, a new algorithm based on the intrinsic shape signature (ISS) feature points combined with the improved iterative closest point (ICP) algorithm is proposed. Firstly, the feature points of point cloud are extracted by the ISS algorithm and described by the fast point feature histograms algorithm. Then, the initial registration of point cloud is completed by using the sample consensus initial alignment algorithm to make the two different angle point clouds obtain a relatively good initial position. Finally, the ICP registration efficiency is promoted by the k-dimension tree nearest neighbor search algorithm. The experimental results show that the proposed algorithm has higher registration accuracy and faster execution speed than the traditional registration algorithms.