A new robust 2D camera calibration method using RANSAC

Abstract Camera calibration is a basic and crucial problem in computer vision and photogrammetry. The traditional calibration approach based on 2D planar target fails to give reliable and accurate results due to the inaccurate localization of feature points in some calibration images. An accurate and robust estimation method for camera parameters based on RANdom SAmple Consensus (RANSAC) algorithm is proposed to exclude the unreliable images in this study. First, the distance between circular point and image of the absolute conic is defined, and the recommended threshold value is given by computer simulation. Second, RANSAC has been utilized to pick out a subset of calibrating images automatically, and linear algebraic approximation is performed to estimate the intrinsic parameters and external parameters. Finally, all the camera parameters including lens distortion parameters are refined by the non-linear searching algorithm. Numerical simulation and practical experiment in this paper demonstrate the accuracy and robustness of the proposed method. The experimental results show that the proposed method is more robust and efficient in improving the calibration accuracy than the traditional methods.