【摘要】 针对常用的平面拟合方法在点云数据存在误差或异常值时产生拟合不稳定的现象,提出了结合最小二乘法的随机抽取一致性(random sample consensus,RANSAC)平面拟合算法.该方法先用RANSAC算法检测并剔除异常数据点,再利用最小二乘法将得到的有效数据点拟合,计算平面模型参数.实验中,分别采用该算法和最小二乘法、特征值法对仿真数据进行平面拟合,且采用本文提出的算法,分别对含有不同程度误差和异常值的点云数据进行拟合计算.研究结果表明:该算法适用于存在误差和异常值的点云数据拟合,能稳定地得到较好的平面参数估值,具有较强的稳健性.更多还原

【Abstract】 In common plane fitting methods for point clouds,the results of planar parameter estimation are not always accurate when the gross errors and outliers are included. To overcome this shortcoming, RANSAC( random sample consensus) algorithm is proposed combined with least square method. The RANSAC algorithm is adopted to detect and eliminate the outlier points,and least square method makes plane fitting for the remaining inner valid points. Analytical simulation experiments have been conducted. Comparative results between our method and traditional methods,such as least squares and eigenvalue method,are provided. The proposed method is also used for solving the point clouds problems with varying scale of errors and outliers. Calculation results verify that the method is adaptive to plane fitting in various point clouds,and it can steadily get fine planar parameters with good robustness.更多还原