基于RANSAC和TLS的点云平面拟合  

Plane fitting of point clouds based on RANSAC and TLS

作　　者：李希[1] 韩燮[1] 熊风光[1] 

机构地区：[1]中北大学计算机与控制工程学院,山西太原030051

出　　处：《计算机工程与设计》2017年第1期123-126,143,共5页Computer Engineering and Design

基　　金：国家科技支撑计划基金项目(2013BAH45F02);国家自然科学基金项目(61379080)

摘　　要：针对点云平面拟合过程中出现的异常值及误差的问题,提出一种将随机采样一致(random sample consensus,RANSAC)算法与整体最小二乘法(total least squares,TLS)相结合的点云平面拟合方法。利用随机采样一致算法剔除异常值,利用整体最小二乘法对剩余有效点进行平面拟合,计算模型参数。实验结果表明,该方法与传统的特征值法、最小二乘法相比,能提高参数的估算精度,更适合对含有不同异常值及误差的点云数据进行拟合,是一种稳健的平面拟合方法。To resolve the problems of outliers and errors in the process of point cloud plane fitting, a method combining the random sample consensus algorithm and total least squares was proposed. Random sample consensus algorithm was used to eliminate outliers, and the total least squares were used to do plane fitting for the remaining valid points. Experiments results verify that the method is more adaptive to fit plane in various point clouds compared to the methods such as least squares and eigenvalue method, and it can improve the estimate precision of the model parameters and it can steadily get fine planar parameters with good robustness.

关 键 词：点云数据 平面拟合 随机采样一致性 整体最小二乘法 异常值 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]