In order to solve the problems of long time consuming and easy failure of the existing coarse registration algorithms based on global registration on two 3D point clouds with low overlap rate, we proposed a coarse registration algorithm based on feature regions and the Super 4-Points Congruent Sets (SUPER4PCS) algorithm. Firstly, intrinsic shape signatures (ISS) algorithm was used to extract and describe the features of the down-sampled point clouds. Secondly, the feature point clouds were divided into regions and the initial overlapped sub-regions were extracted. Thirdly, the complete overlapping regions were grown from the overlapped sub-regions and gradually recovered. Finally, the SUPER4PCS was used for registration on the complete overlapping regions. The experimental results showed that the geometric accuracy, registration success rate, and robustness of the proposed algorithm were better than that of SUPER4PCS and its improved algorithms, and the time consumption was one order of magnitude lower than that of SUPER4PCS on point clouds with low overlap rate.

为了解决现有基于全局配准的两个重叠率低的3D点云的粗配准算法耗时长、容易失效的问题，提出了一种基于特征区域和超4点全等的粗配准算法。设置 (SUPER4PCS) 算法。首先，使用内在形状签名（ISS）算法来提取和描述下采样点云的特征。其次，对特征点云进行区域划分，提取初始重叠的子区域。第三，完全重叠的区域从重叠的子区域中生长出来并逐渐恢复。最后，SUPER4PCS 用于在完全重叠区域上进行配准。实验结果表明，几何精度、配准成功率、