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. 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 chapter, 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.
KeywordsExtend Kalman Filter Calibration Parameter Camera Calibration Visual Odometry Sequential Probability Ratio Test
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