Uncertainty in pose estimation: a Bayesian approach

We propose a the use of a consistent Bayesian methodology for the analysis of the uncertainty associated with a pose estimation procedure. A novel model-based technique to estimate the pose of rigid 3D objects from laser range finder images is studied, and various sources of uncertainty are carried through the process using a Bayesian MAP treatment, yielding local, point-by-point estimates of position and predicted error. Promising experimental results on complex objects are presented and discussed.

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