Dynamic stereo is useful for constructing a complete map of the environment as only a portion of the actual environment is visible from each viewpoint. In addition, there is usually an overlap between the portions of the environment visible from two successive viewpoints. It is then feasible to utilize a prediction-verification approach to combine the individual depth estimates of features visible from both viewpoints to obtain a more accurate estimate. A fundamental requirement for such an approach to be used is accurate knowledge of the camera motion between the two viewpoints. A robust least median of squares (LMS)-based algorithm to recover this motion which provides a self-calibration mechanism is presented. The recovered motion is utilized for recursive disparity prediction and refinement using a robustified Kalman filter formulation. Results are presented for a laboratory stereo sequence.
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