Point Cloud Plane Fitting Based on RANSAC and Robust Eigenvalue Method

Abstract

Aiming at the problem of outliers and errors in the process of point cloud plane fitting, a point cloud plane fitting method combining random sampling consensus algorithm and an improved eigenvalue algorithm is proposed. The random sampling consensus algorithm is used to eliminate outliers, and the improved robust eigenvalue algorithm is used to fit the remaining effective points and calculate the plane parameters. The experimental results show that compared with the traditional eigenvalue method, least squares method and RANSAC algorithm, this method can improve the estimation accuracy of parameters, and is more suitable for fitting point cloud data with different outliers and errors. It is an ideal plane fitting method.