利用MPI实现多幅点云ICP并行配准

迭代最近点算法（ICP）是一种用于点云精确配准的经典算法。针对多幅点云进行ICP配准存在耗时多、效率低的问题，本文利用消息传递接口MPI对多幅点云进行分批并行配准。首先并行求解相邻两幅点云的相邻变换矩阵，然后计算每幅点云在当前批次的局部变换矩阵，最后获得每幅点云的全局变换矩阵。本文以DELL PowerEdge R730服务器为计算平台，对空间点总规模达四千多万的65幅点云进行了分批并行配准。试验结果表明：利用MPI对多幅点云进行分批处理可显著加快配准速度，最优进程数为计算机的核数时，加速比为5.3。

ICP parallel registration of multiple point clouds with MPI

Iterative closest point( ICP) algorithm is a classic algorithm for precise point cloud registration. In this paper,in order to solve the problem of time-consuming and low efficiency in ICP registration of multiple point clouds,the message passing interface( MPI) is proposed to perform batch parallel registration for multiple point clouds. First,the adjacent transformation matrix of two adjacent point clouds is solved in parallel,then the local transformation matrix of each point cloud in the current batch is calculated,and finally the global transformation matrix of each point cloud is obtained. 65 chip point clouds with a total size of over 40 million points in space are registered in batch parallel with DELL PowerEdge R730 server as the computing platform. The results show that batch processing of multiple point clouds using MPI can significantly accelerate the registration speed,and the acceleration ratio is 5.3 when the optimal number of processes is the kernel of the computer.