Title: On Handling Uncertainty in the Fundamental Matrix for Scene and Motion Adaptive Pose Recovery
Authors: Sreenivas R. Sukumar, Hamparsum Bozdogan, David L. Page, Andreas F. Koschan and Mongi A. Abidi
Abstract:
The estimation of the fundamental matrix is the key step in feature-based camera ego-motion estimation for applications in scene modeling and vehicle navigation. In this paper, we present a new method of analyzing and further reducing the risk in the fundamental matrix due to
the choice of a particular feature detector, the choice of the matching algorithm, the motion model, iterative hypothesis generation and verification paradigms. Our scheme makes use of model-selection theory to guide the switch to optimal methods for fundamental matrix
estimation within the hypothesis-and-test architecture. We demonstrate our proposed method for vision-based robot localization in large-scale environments where the environment is constantly changing and navigation within the environment is unpredictable.
Link:
CVPR2008 Paper
http://imaging.utk.edu/publications/papers/2008/CVPR08_ss.pdf
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