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Published December 1994 | Published
Book Section - Chapter Open

Motion Estimation via Dynamic Vision


Estimating the 3D motion of an object from a sequence of projections is of paramount importance in a variety of applications in control and robotics. Although "visual motion estimation" is an old problem, only recently tools from control and estimation theory have hinted at acceptable solutions. Moreover, the problem raises a number of issues of system theoretic interest, such as nonlinear estimation and identification on topological manifolds and observability in a projective geometric framework. In this paper we analyze a formulation of the visual motion estimation problem in terms of identification of nonlinear implicit systems with parameters on the so-called "essential manifold"; the estimation is performed either in the local coordinates or in the embedding space of the parameter manifold.

Additional Information

© 1994 IEEE. We wish to thank Prof. J.K. Åström for his discussions on implicit Kalman filtering, Prof. Richard Murray and Prof. Shankar Sastry for their observations and useful suggestions. Also discussions with Michiel van Nieuwstadt and Andrea Mennucci were helpful. This research has been funded by the California Institute of Technology, a scholarship from the University of Padova, a fellowship from the "A. Gini" Foundation, an AT&T Foundation Special Purpose grant, ONR grant N0014-93-1-0990, grant ASI-RS-103 from the Italian Space Agency and the National Young Investigator Award (P.P.). A long version of this paper has been submitted to the IEEE Transactions on Automatic Control.

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August 20, 2023
August 20, 2023