一种空间非合作目标的稀疏点云配准算法

点云数据配准是三维重构的关键技术之一，为了提高空间非合作目标的稀疏扫描点云数据配准的速度和精度，提出一种改进的基于四点算法的全局配准算法进行初始配准，再使用迭代最近点算法精确配准.针对直接扫描所得到点云数据量大的问题，本文提出一种基于KD Tree点云均匀采样简化算法，并且对传统基于四点算法中的阈值参数进行了统一，确定了各误差阈值参数和点云密度之间的关系.仿真结果表明，该方法能够快速、有效地实现卫星稀疏点云的配准，改进的四点算法配准耗时仅为几何哈希算法的42.49%.

A Sparse Point Cloud Registration Algorithm of Non Cooperative Spacecraft

The point cloud data registration is one of the key technologies of three dimensional reconstruction. To solve the registration issue of sparse point cloud scanned from the non cooperative spacecraft, we propose a improved 4 points congruent sets (4PCS) algorithm to obtain the preliminary registration result, and optimize the final alignment with the improved iterated closest points (ICP) algorithm. Then，a novel point cloud simplification algorithm using uniform sampling is proposed based on KD Tree. The uniform relation of the threshold parameters is established via the density of the point cloud. The results show that the proposed algorithm can effectively achieve good alignments of the sparse point cloud of the satellite, and the consuming time is decreased to 42.49% compared with the Geometric Hashing algorithm.