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Summary: 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.
1. Introduction
The fundamental matrix F that relates two perspective
images of a single rigid object/scene is estimated by
solving the epipolar constraint in Equation 1, where im~
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