一种新型的基于点域特征与加权投票的三维点云匹配算法  

A Novel Three-Dimensional Point Cloud Matching Algorithm Based on Point Region Features and Weighted Voting

作　　者：陆俊君 丁克 赵祚喜[1] 王丰[2] Lu Junjun;Ding Ke;Zhao Zuoxi;Wang Feng(College of Engineering,South China Agricultural University,Guangzhou 510640,Guangdong,China;Guangdong University of Technology,Guangzhou 510006,Guangdong,China)

机构地区：[1]华南农业大学工程学院,广东广州510640 [2]广东工业大学,广东广州510006

出　　处：《激光与光电子学进展》2025年第2期225-234,共10页Laser & Optoelectronics Progress

基　　金：佛山市科技创新项目(FS0AA-KJ919-4402-0074)。

摘　　要：针对传统PPF(Point pair features)算法在精密工业生产中点云匹配精度不足以及对平面点云鲁棒性不强等问题,设计一种新型的点域特征PRF(Point regions features)配准方法。其中,PRF点域特征通过将目标点对所在邻域的特征复杂度与平均方向作为补充特征进行匹配,根据不同点域的复杂度作为加权标准对特征匹配程度进行加权投票,然后在真实工作场景中获取点云。实际场景中常见点云匹配实验结果表明,所提PRF配准算法能够在基本不影响速度的情况下显著提高点云精度与鲁棒性。The traditional point pair features(PPF)algorithm lacks sufficient point cloud matching accuracy in precision industrial production and robustness to planar point clouds.To address these issues,this study proposes a novel point regions features(PRF)registration method.In this method,PRF point domain features enhance matching by incorporating the feature complexity and average direction of target point pairs within their respective neighborhoods as complementary features.The algorithm utilizes the complexity of different point domains as a weighted criterion for feature matching,conducting a weighted voting process.The point cloud is then obtained in the real working scene.Experimental results from common point cloud matching experiments in real-world scenarios show that the proposed PRF registration algorithm significantly improves point cloud accuracy and robustness with minimal impact on speed.

关 键 词：三维机器视觉 点云匹配 加权投票 鲁棒性 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]