基于随机抽样一致性算法的稳健点云平面拟合方法

A RANSAC-based stable plane fitting method of point clouds

针对点云数据平面拟合过程中存在粗差及异常值等问题,文章提出一种基于随机抽样一致性算法(RANSAC)的稳健平面拟合方法。该方法以RANSAC算法为基础并结合特征值法,通过设置一定的准则,剔除点云数据中存在的粗差及异常值,达到获得理想平面拟合参数的目的。运用此算法对仿真数据及实测数据进行平面拟合,并与传统算法进行比较,结果表明该方法可以很好地适应于点云数据中存在粗差及异常值的情况,获得较好的平面参数估计值,是一种稳健的平面拟合算法。

In the process of plane fitting of point clouds,there are some gross errors and outliers.In order to overcome this shortcoming,a robust plane fitting method based on RANSAC（RANdom SAmple Consensus）was proposed in the paper.It combined with eigenvalue method and obtained some ideal plane fitting parameters through setting certain criteria to remove the gross errors and outliers.The proposed algorithm was used to do plane fitting with the simulation data and the measured data,and result showed that compared with traditional methods,the proposed method could do well with the point clouds containing errors and outliners,thus be a robust plane fitting algorithm.