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Proceedings Paper

Estimating global uncertainty in epipoloar geometry for vehicle-mounted cameras
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Paper Abstract

We present a method for estimating the global uncertainty of epipolar geometry with applications to autonomous vehicle navigation. Such uncertainty information is necessary for making informed decisions regarding the confidence of a motion estimate, since we must otherwise accept the estimate without any knowledge of the probability that the estimate is in error. For example, we may wish to fuse visual estimates with information from GPS and inertial sensors, but without uncertainty information, we have no principled way to do so. Ideally, we would perform a full search over the 7-dimensional space of fundamental matrices to yield an estimate and its related uncertainty. However, searching this space is computationally infeasible. As a compromise between fully representing posterior likelihood over this space and producing a single estimate, we represent the uncertainty over the space of translation directions in a calibrated framework. In contrast to finding a single estimate, representing the posterior likelihood is always a well-posed problem, albeit an often computationally challenging one. Given the posterior likelihood, we derive a confidence interval around the motion estimate. We verify the correctness of the confidence interval using synthetic data and show examples of uncertainty estimates using vehicle-mounted camera sequences.

Paper Details

Date Published: 9 May 2006
PDF: 12 pages
Proc. SPIE 6230, Unmanned Systems Technology VIII, 62301L (9 May 2006); doi: 10.1117/12.666408
Show Author Affiliations
David Nistér, Univ. of Kentucky (United States)
Christopher Engels, Univ. of Kentucky (United States)


Published in SPIE Proceedings Vol. 6230:
Unmanned Systems Technology VIII
Grant R. Gerhart; Charles M. Shoemaker; Douglas W. Gage, Editor(s)

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