A step towards self-calibration in SLAM: Weakly calibrated on-line structure and motion estimation

We propose a structure and motion estimation scheme based on a dynamic systems approach, where states and parameters in a perspective system are estimated. An online method for structure and motion estimation in densely sampled image sequences is presented. The proposed method is based on an extended Kalman filter and a novel parametrization. We derive a dynamic system describing the motion of the camera and the image formation. By a change of coordinates, we represent this system by normalized image coordinates and the inverse depths. Then we apply an extended Kalman filter for estimation of both structure and motion. Furthermore, we assume only weakly calibrated cameras, i.e. cameras with unknown and possibly varying focal length, unknown and constant principal point and known aspect ratio and skew. The performance of the proposed method is demonstrated in both simulated and real experiments. We also compare our method to the one proposed by Civera et al. and show that we get superior results.