Easy to calib: Auto-calibration of camera from sequential images based on VP and EKF

Abstract

Camera calibration is an important issue in computer vision. In this paper, we propose an improved camera auto-calibration algorithm from sequential images based on VP (vanishing point) and EKF (extended Kalman filter) to determine camera intrinsic parameters. This is the first vanishing point-based auto-calibration algorithm, which only uses a sequence of monocular images as input without any other information. According to geometry constraints of projective projection, we compute the vanishing points in three orthogonal directions by observing an object moving in one direction from an image sequence. Afterwards intrinsic parameters can be calculated. The extended Kalman filter is used to track the feature points in the image sequence rapidly and accurately. Compared with existing methods based on vanishing point, our approach simplifies calibration process, gets rid of calibration objects and manual intervention, avoids correspondences between 2D image and 3D world features and reduces errors to a large extent. Simulations and real image experiments validate the proposed approach and indicate that it is accurate and robust to noise. As a result, it could be applied to almost all real scenes like on-orbit camera calibration, autonomous vehicle navigation, space vehicle rendezvous and docking.