针对在不同视角下所获得的三维点云数据，提出了一种基于特征提取的点云自动配准算法。算法根据点云在不同半径内的法向量变化度来提取特征点，综合利用点云局部点的三种几何特征搜索匹配点对。通过利用距离约束条件来获取准确匹配点对并计算初始配准参数。精确配准阶段采用改进的迭代最近点(ICP)算法完成二次拼接。实验结果表明，与传统ICP算法相比，该算法在运行时间与精确度上都有着明显的提升。

A automatic registration algorithm for point cloud based on feature extraction is presented for threedimensional point cloud data obtained under different views. The algorithm extracts the feature points according to the variation of normal vector within various radius, and finds matching point pairs by using three geometric features of local point clouds comprehensively. The accurate matching point pairs by using distance restriction are obtained and the initial registration parameters are calculated. During the phase of accurate registration, the improved iterative closest point (ICP) should be used to finish the second mosaicking. The experimental results show that the proposed algorithm is more effective than traditional ICP in terms of run time and accuracy.