针对低质量点云的点云配准算法  

Point Cloud Registration Algorithm for Low-quality Point Cloud

作　　者：吴振慧[1] WU Zhen-hui(Yangzhou Polytechnic College,Yangzhou 225009,China)

机构地区：[1]扬州职业大学,江苏扬州225009

出　　处：《扬州职业大学学报》2023年第1期17-21,34,共6页Journal of Yangzhou Polytechnic College

摘　　要：在点云数据采集过程中,现场情况会导致点云数据质量下降,出现如点云残缺、点云稀疏、噪声等问题,传统点云配准算法在对低质量点云进行配准时会出现配准失败的问题。针对低质量点云配准的挑战,提出一种粗匹配和精匹配结合的算法,通过PPF方法进行粗匹配,将其计算结果作为ICP精匹配方法的迭代初值,提高算法的速度和精度。最后,分别针对稀疏点云、含噪声点云和残缺点云进行实验验证,证明了本算法的有效性。In the process of point cloud data collection,the on-site situation will lead to the deterioration of point cloud data quality,such as point cloud incompleteness,point cloud sparseness,noise and other problems,and the traditional point cloud registration algorithm will fail to register low quality point cloud.Aiming at the challenge of low-quality point cloud registration,this paper proposes an algorithm combining coarse matching and fine matching.The PPF method is used for coarse matching,and the calculated results are taken as the initial iteration value of ICP fine matching method,which improves the speed and accuracy of the algorithm.Finally,experiments are carried out for sparse point cloud,noisy point cloud and incomplete point cloud respectively,which proves the effectiveness of the proposed algorithm.

关 键 词：低质量点云 点云配准 粗匹配 精匹配 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]