Accurate and robust estimation of camera parameters using RANSAC

Camera calibration plays an important role in the field of machine vision applications. The popularly used calibration approach based on 2D planar target sometimes fails to give reliable and accurate results due to the inaccurate or incorrect localization of feature points. To solve this problem, an accurate and robust estimation method for camera parameters based on RANSAC algorithm is proposed to detect the unreliability and provide the corresponding solutions. Through this method, most of the outliers are removed and the calibration errors that are the main factors influencing measurement accuracy are reduced. Both simulative and real experiments have been carried out to evaluate the performance of the proposed method and the results show that the proposed method is robust under large noise condition and quite efficient to improve the calibration accuracy compared with the original state.

► An accurate and robust estimation method for camera parameters is proposed.
► Both the threshold selection method and RANSAC are used to detect inaccurate feature points.
► Most of the outliers can be successfully detected and removed.
► The calibration errors are reduced effectively through the proposed method.