基于最小平方中值的点云平面拟合算法  

Point Cloud Plane Fitting Algorithm Based on Least Square Median

作　　者：王洋[1] 王俊元[1] 杜文华[1] 段能全[1] Wang Yang;Wang Junyuan;Du Wenhua;Duan Nengquan(School of Mechanical Engineering,North University of China,Taiyuan 030051,Shanxi,China)

机构地区：[1]中北大学机械工程学院,山西太原030051

出　　处：《激光与光电子学进展》2023年第4期237-244,共8页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(51905496)。

摘　　要：三维扫描获取待测对象点云时,不可避免地会出现噪声点和异常值,严重影响点云平面参数估计和平面拟合精度。随机抽样一致性(RANSAC)和主成分分析(PCA)结合的算法可以有效估计点云平面参数并拟合平面,具有一定鲁棒性,但RANSAC算法每次迭代时都需要判断以区分内点与外点,具有冗余性,对运行效率有一定影响,同时其估计结果也会受到迭代次数的影响。针对以上问题,提出了一种结合最小平方中值(LMedS)和PCA的算法拟合点云平面,并选取3种点云模型进行实验,分别为Semantic3D户外场景点云数据库、线激光传感器获取的零件表面点云及普林斯顿大学的室内数据集。实验结果表明,在十万数量级点云中,LMedS算法可以有效估计点云平面参数,与RANSAC算法相比,LMedS算法不仅可以有效估计平面模型,且运行速度有一定提高,耗时少,两者的精度相当,是一种具有较强鲁棒性和优势性的点云平面拟合算法。When obtaining the point cloud of an object to be measured by threedimensional scanning,noise points and outliers will inevitably appear,which will significantly affect the accuracy of point cloud plane parameter estimation and plane fitting.An algorithm that combines random sampling consensus(RANSAC)and principal component analysis(PCA)can effectively estimate point cloud plane parameters and fit the plane,with some degree of robustness.However,the RANSAC algorithm needs to judge in each iteration process to distinguish between inner and outer points,which introduces redundancy and affects operation efficiency.Furthermore,its estimation results will be affected by the number of iterations.To solve the above problems,an algorithm that combines least square median(LMedS)and PCA is proposed to fit the point cloud plane,and three point cloud models are selected for experiments:Semantic3D outdoor scene point cloud database,part surface point cloud obtained using a line laser sensor,and indoor dataset of Princeton University.The experimental results show that,in the 100000 order of magnitude point cloud,the LMedS algorithm can effectively estimate the plane parameters of point clouds.Compared with the RANSAC algorithm,the LMedS algorithm can effectively estimate a plane model,with increased running speed,in less time,and with the same accuracy.The proposed method is a point cloud plane fitting method with strong robustness and advantages.

关 键 词：图像处理 点云 随机抽样一致性 主成成分分析 最小平方中值 平面拟合 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]