Computer vision researchers have proved the feasibility of camera self-calibration -the estimation of a camera's internal parameters from an image sequence without any known scene structure. Various self-calibration algorithms have been published. Nevertheless, all of the recent sequential approaches to 3D structure and motion estimation from image sequences which have arisen in robotics and aim at real-time operation (often classed as visual SLAM or visual odometry) have relied on pre-calibrated cameras and have not attempted online calibration. In this paper, we present a sequential filtering algorithm for simultaneous estimation of 3D scene estimation, camera trajectory and full camera calibration from a sequence of fixed but unknown calibration. This calibration comprises the standard projective parameters of focal length and principal point along with two radial distortion coefficients. To deal with the large non-linearities introduced by the unknown calibration parameters, we use a Sum of Gaussians (SOG) filter rather than the simpler Extended Kalman Filter (EKF). To our knowledge, this is the first sequential Bayesian autocalibration algorithm which achieves complete fixed camera calibration using as input only a sequence of uncalibrated monocular images. The approach is validated with experimental results using natural images, including a demonstration of loop closing for a sequence with unknown camera calibration.
In REF the authors present an online approach to self-calibration, which employs a Sum-of-Gaussians filter for visual SLAM.
Camera self-calibration for sequential Bayesian structure from motion
2009 IEEE International Conference on Robotics and Automation
2009 IEEE International Conference on Robotics and Automation
Computer Science