  Camera localization, i.e., camera pose regression, represents an important
task in computer vision since it has many practical applications such as in the
context of intelligent vehicles and their localization. Having reliable
estimates of the regression uncertainties is also important, as it would allow
us to catch dangerous localization failures. In the literature, uncertainty
estimation in Deep Neural Networks (DNNs) is often performed through sampling
methods, such as Monte Carlo Dropout (MCD) and Deep Ensemble (DE), at the
expense of undesirable execution time or an increase in hardware resources. In
this work, we considered an uncertainty estimation approach named Deep
Evidential Regression (DER) that avoids any sampling technique, providing
direct uncertainty estimates. Our goal is to provide a systematic approach to
intercept localization failures of camera localization systems based on DNNs
architectures, by analyzing the generated uncertainties. We propose to exploit
CMRNet, a DNN approach for multi-modal image to LiDAR map registration, by
modifying its internal configuration to allow for extensive experimental
activity on the KITTI dataset. The experimental section highlights CMRNet's
major flaws and proves that our proposal does not compromise the original
localization performances but also provides, at the same time, the necessary
introspection measures that would allow end-users to act accordingly.
