  This work focuses on improving uncertainty estimation in the field of object
classification from RGB images and demonstrates its benefits in two robotic
applications. We employ a (BNN), and evaluate two practical inference
techniques to obtain better uncertainty estimates, namely Concrete Dropout
(CDP) and Kronecker-factored Laplace Approximation (LAP). We show a performance
increase using more reliable uncertainty estimates as unary potentials within a
Conditional Random Field (CRF), which is able to incorporate contextual
information as well. Furthermore, the obtained uncertainties are exploited to
achieve domain adaptation in a semi-supervised manner, which requires less
manual efforts in annotating data. We evaluate our approach on two public
benchmark datasets that are relevant for robot perception tasks.
