The paper describes an iterative algorithm that estimates the motion of a camera through an environment directly from brightness derivatives of an image pair. A global ego-motion constraint is combined with the local brightness constancy constraint to relate local surface models with the global ego-motion model and local brightness derivatives. In an iterative process, the author first refines the local surface models using the ego-motion as a constraint, and then refines the ego-motion model using the local surface models as constraints. He performs this analysis at multiple resolutions. He shows how information from local corner-like and edge-like image structures contribute to the refinement of the global ego-motion estimate, and how the ego-motion constraint can help resolve local motion ambiguities that arise from the aperture problem. Results of the algorithm are shown on uncalibrated outdoor image sequences, and also on a computer-rendered image sequence.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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K. Hanna (2002) studied this question.
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