Camera localization, i.e., camera pose regression, represents a very
important task in computer vision, since it has many practical applications,
such as autonomous driving. A reliable estimation of the uncertainties in
camera localization is also important, as it would allow to intercept
localization failures, which would be dangerous. Even though the literature
presents some uncertainty estimation methods, to the best of our knowledge
their effectiveness has not been thoroughly examined. This work compares the
performances of three consolidated epistemic uncertainty estimation methods:
Monte Carlo Dropout (MCD), Deep Ensemble (DE), and Deep Evidential Regression
(DER), in the specific context of camera localization. We exploited CMRNet, a
DNN approach for multi-modal image to LiDAR map registration, by modifying its
internal configuration to allow for an extensive experimental activity with the
three methods on the KITTI dataset. Particularly significant has been the
application of DER. We achieve accurate camera localization and a calibrated
uncertainty, to the point that some method can be used for detecting
localization failures