基于LMedS的WTLSD拟合平面算法研究  

Research on Fitting Plane Algorithm Based on LMeds-WTLSD

作　　者：任永强[1] 臧昌禹 胡长路 REN Yongqiang;ZANG Changyu;HU Changlu(School of Mechanical Engineering,Hefei University of Technology,Hefei 230009,China)

机构地区：[1]合肥工业大学机械工程学院,合肥230009

出　　处：《组合机床与自动化加工技术》2025年第4期83-86,共4页Modular Machine Tool & Automatic Manufacturing Technique

基　　金：安徽省重点研发和开发计划项目(202304a05020079)。

摘　　要：针对实际点云数据中存在的噪点与缺陷对拟合平面时带来的影响,提出一种基于最小平方中值算法(least median of squares,LMedS)与距离加权总体最小二乘法(weighted total least squares based on distance,WTLSD)相结合的平面拟合算法。通过最小平方中值算法初步去除点云中的噪点,并基于距离构建初始权重矩阵,利用距离加权总体最小二乘法对点云进行平面拟合,减少平面中凸起与凹陷等缺陷对平面拟合的影响,该算法与传统平面拟合算法相比具备消除异常点与平面缺陷的优点,具备更高的拟合精度;与随机采样一致性算法(random sample consensus,RANSAC)相比具有更高的拟合效率与相近的拟合精度。In view of the influence of noise and defects in the actual point cloud data on the plane fitting,proposing a plane fitting algorithm based on the combination of least median of squares(LMedS)and weighted total least squares method.In this paper,the noise in the point cloud is preliminarily removed by the least squares median algorithm,and the initial weight matrix is constructed based on the distance,and the weighted total least squares based on distance(WTLSD)method is used Compared with the traditional plane fitting algorithm,the proposed algorithm has the advantages of eliminating anomalies and plane defects,and has higher fitting accuracy,and has faster fitting speed and similar fitting accuracy than the random sample consensus(RANSAC)algorithm.

关 键 词：点云数据 噪点 平面拟合 最小平方中值算法(LMedS) 距离加权总体最小二乘法(WTLSD) 

分 类 号：TH16[机械工程—机械制造及自动化] TG659[金属学及工艺—金属切削加工及机床]