Self-calibration approach to stereo cameras with radial distortion based on epipolar constraint.

In this paper, we propose a self-calibration approach to stereo cameras with radial distortion from stereo image pairs of a common 3D scene. Based on the epipolar constraint in the stereo image pair, the intrinsic and extrinsic parameters of stereo cameras are estimated synchronously with a minimum number of nine image point correspondences. It is significant within a random sample consensus (RANSAC) scheme to cope with the outliers of feature matches efficiently and robustly. Then the inliers of the stereo image pair that have been determined after RANSAC are used to optimize the calibration parameters of stereo cameras. Furthermore, more accurate calibration results can be achieved with the joint optimization of multiple stereo image pairs. Both synthetic and real data are used to evaluate the performance of the proposed method, demonstrating that our method can calibrate stereo cameras with radial distortions efficiently and accurately.