  Uncertainty quantification is essential for robotic perception, as
overconfident or point estimators can lead to collisions and damages to the
environment and the robot. In this paper, we evaluate scalable approaches to
uncertainty quantification in single-view supervised depth learning,
specifically MC dropout and deep ensembles. For MC dropout, in particular, we
explore the effect of the dropout at different levels in the architecture. We
show that adding dropout in all layers of the encoder brings better results
than other variations found in the literature. This configuration performs
similarly to deep ensembles with a much lower memory footprint, which is
relevant forapplications. Finally, we explore the use of depth uncertainty for
pseudo-RGBD ICP and demonstrate its potential to estimate accurate two-view
relative motion with the real scale.
