关键词: 随机抽样一致性, 特征值法, 点云, 平面拟合

Abstract: It is hard to fit points cloud with exceptional points by classical plane fitting algorithm.A robust plane fitting algorithm based on RANSAC and eigenvalue method is advanced to solve this problem.Three points are selected randomly to compute plane parameters，and the number of inner points is counted with proper threshold.This processing is repeated for certain times and points group with the most inner points is selected，with these points eigenvalue method is used to plane fitting.Fitting experiments are carried out with various kinds of error and exceptional points，after that it is used in rock mass discontinuity points cloud on 3-D rock tunnel face digital modal，which is generated with binocular system.Conclusions are drawn that compared with classical methods the proposed method can adapt various situation of error and exception and get fine planar parameters steadily.It is a robust plane fitting algorithm.

Key words: RANdom Sample Consensus（RANSAC）, eigenvalue method, points cloud, plane fitting