A comparison of uncertainty estimation approaches for DNN-based camera localization

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.