In traditional plane fitting methods for point clouds, people don' t consider errors in data and in coefficients matrix simultaneously, which will result in incorrectness of plane parameters. In order to overcome this shortcoming, a new method for fitting local plane to point clouds was proposed. The method is based on total least squares. In consideration of the errors in all observation data, we tried to delete outliers from point clouds, and thus obtained a robust solution to plane fitting parameter. Analytical experiments based on simulated data and real data were conducted, and comparisons between the method and traditional methods such as least square method and eigenvalue method were also implemented. The results show that the method has the capability to overcome bad influence from outliers, and to increase the reliability of parameters estimation.%针对点云数据平面拟合方法没有完整考虑测量数据中的误差及系数阵中误差的情况,提出稳健整体最小二乘点云数据平面拟合方法.该法以整体最小二乘法为基础,在考虑全部观测量存在误差的情况下,通过一定的准则删除数据中的粗差或异常值,从而获得稳健的平面参数估值.实验中,分别利用最小二乘法、特征值法和稳健整体最小二乘拟合法对仿真点云数据和真实点云数据进行平面拟合,结果显示该法能克服异常值的影响,得到可靠的平面参数估值,具有稳健性.
展开▼
