Title:
Uncertainty Quantification in the context of 6D Pose Estimation
Uncertainty Quantification in the context of 6D Pose Estimation
Authors
Boumerdassi, Maya
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Pradalier, Cédric
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Abstract
We use the work on Deep Evidential Regression that was initially developed in the context of simple regression in R, and extend it to work on higher dimensional groups and manifolds with a Lie algebra structure. We develop a general framework for the Deep Evidential Loss and assess how well the models perform on several manifolds, identify and discuss several limitations encountered through experiments. Finally, one of the major goals of this thesis is to assess whether we could be use the Deep Evidential method in the context of 6D Object Pose Estimation, where it can be crucial to have both information on the prediction and the uncertainty on the prediction (really important for safety-critical robotic manipulation).
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Date Issued
2022-05-03
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