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9 May 2006 Estimating global uncertainty in epipoloar geometry for vehicle-mounted cameras
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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.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
David Nistér and Christopher Engels "Estimating global uncertainty in epipoloar geometry for vehicle-mounted cameras", Proc. SPIE 6230, Unmanned Systems Technology VIII, 62301L (9 May 2006);


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