利用MPI实现多幅点云ICP并行配准

doi:10.13474/j.cnki.11-2246.2021.0083

测绘通报 ›› 2021, Vol. 0 ›› Issue (3): 87-90.doi: 10.13474/j.cnki.11-2246.2021.0083

利用MPI实现多幅点云ICP并行配准

崔家武¹, 周波阳², 张兴福², 柴向浩¹, 樊树春¹

1. 广州市城市规划勘测设计研究院, 广东广州 510060;
2. 广东工业大学测绘工程系, 广东广州 510006

收稿日期:2019-12-09 修回日期:2020-04-15 出版日期:2021-03-25 发布日期:2021-04-02
通讯作者: 周波阳。E-mail:wdzbyang@163.com
作者简介:崔家武(1992—),男,硕士,主要研究方向为测绘数据并行处理。E-mail:864885814@qq.com
基金资助:
国家自然科学基金（41504013）；NSFC-广东联合基金（第二期）超级计算科学应用研究专项资助国家超级计算广州中心支持

ICP parallel registration of multiple point clouds with MPI

CUI Jiawu¹, ZHOU Boyang², ZHANG Xingfu², CHAI Xianghao¹, FAN Shuchun¹

1. Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510060, China;
2. Department of Surveying and Mapping, Guangdong University of Technology, Guangzhou 510006, China

Received:2019-12-09 Revised:2020-04-15 Online:2021-03-25 Published:2021-04-02

摘要/Abstract

摘要： 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。

关键词: MPI, ICP, 多幅点云, 并行配准, 变换矩阵

Abstract: 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.

Key words: MPI, ICP, multiple point clouds, parallel registration, transformation matrix

中图分类号:

P237

崔家武, 周波阳, 张兴福, 柴向浩, 樊树春. 利用MPI实现多幅点云ICP并行配准[J]. 测绘通报, 2021, 0(3): 87-90.

CUI Jiawu, ZHOU Boyang, ZHANG Xingfu, CHAI Xianghao, FAN Shuchun. ICP parallel registration of multiple point clouds with MPI[J]. Bulletin of Surveying and Mapping, 2021, 0(3): 87-90.

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利用MPI实现多幅点云ICP并行配准

ICP parallel registration of multiple point clouds with MPI

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