Research on GPU-based parallel ICP point cloud registration algorithm

Journal Information

J. Hefei Univ. Tech. (Nat. Sci.), Nov.2023, Vol.46 No.11: 1501-1505

DOI: 10.3969/j.issn.1003-5060.2023.11.010

Authors

WANG Jiachen $ ^{1} $, YE Zhourun $ ^{1} $, OU Xin $ ^{2} $, YUAN Bin $ ^{3} $, WU Yan'an $ ^{3} $, ZHANG Shufeng $ ^{3} $

(1. School of Civil and Hydraulic Engineering, Hefei University of Technology, Hefei 230009, China; 2. China-Malaysia Industrial Park (Guangxi) Digital City Technology Co., Ltd., Qinzhou 535008, China; 3. Anhui Kaiyuan Highway and Bridge Co., Ltd., Hefei 230093, China)

Abstract and Keywords

Abstract: In response to the problem of low efficiency of traditional serial fine registration algorithms in calculating massive point cloud data, this paper parallelizes the traditional algorithm by leveraging the multi-thread computing capability of graphics processing unit (GPU). The parallelized algorithm, called compute unified device architecture iterative closest point (CUDAICP) algorithm, is implemented on GPU. Firstly, coarse registration methods are used to perform initial rotation and translation on the source point cloud, obtaining its initial position. Then, the target point cloud is input into the CUDAICP algorithm for fine registration. Experiments on room point clouds and room point clouds with stairs demonstrate that the sample consensus initial alignment (SAC-IA) coarse registration algorithm has good performance in different scenarios. In terms of fine registration, the CUDAICP algorithm achieves up to 8.2 times faster speed compared to the traditional iterative closest point (ICP) algorithm while maintaining accuracy.

Keywords: coarse registration; compute unified device architecture(CUDA); iterative closest point (ICP) algorithm; fine registration; point cloud registration

作者信息

王嘉琛 $ ^{1} $，叶周润 $ ^{1} $，欧鑫 $ ^{2} $，袁斌 $ ^{3} $，吴言安 $ ^{3} $，张树峰 $ ^{3} $

（1. 合肥工业大学土木与水利工程学院，安徽合肥 230009；2. 广西中马园区数字城市科技有限公司，广西钦州 535008；3. 安徽开源路桥有限责任公司，安徽合肥 230093）

摘要和关键词

摘要: 针对传统串行精配准算法在海量点云数据配准时计算效率低的问题, 文章利用图形处理器(graphics processing unit, GPU)的多线程计算能力将传统算法并行化, 基于GPU实现并行化的统一计算设备架构迭代最近点(compute unified device architecture iterative closest point, CUDAICP)算法。首先采用粗配准方法对源点云进行旋转平移, 得到源点云的初始位置, 再将其与目标点云输入CUDAICP算法进行精配准; 对房间点云、带有楼梯的房间点云2种场景点云数据进行配准实验。结果表明: 在粗配准中, 采样一致性初始配准(sample consensus initial alignment, SAC-IA)算法在不同场景下具有较好的效果; 在精配准中, CUDAICP算法与传统迭代最近点(iterative closest point, ICP)算法相比, 在保证精度的同时, 速度提升最高可达8.2倍。

关键词: 粗配准；统一计算设备架构（CUDA）；迭代最近点（ICP）算法；精配准；点云配准

Funding

基金项目：国家自然科学基金资助项目（42074011）；国家自然科学基金青年科学基金资助项目（42204052）；安徽省自然科学基金资助项目（2008085MD115）和大地测量与地球动力学国家重点实验室开放研究基金资助项目（SKLGED2022-1-4）

合肥工业大学学报(自然科学版)

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