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Uncertainty in pose estimation: a Bayesian approach

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3 Author(s)
Callari, F.G. ; Centre for Intelligent Machines, McGill Univ., Montreal, Que., Canada ; Soucy, G. ; Ferrie, F.P.

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

Published in:

Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on  (Volume:2 )

Date of Conference:

16-20 Aug 1998

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