  We present an uncertainty learning framework for dense neural simultaneous
localization and mapping (SLAM). Estimating pixel-wise uncertainties for the
depth input of dense SLAM methods allows re-weighing the tracking and mapping
losses towards image regions that contain more suitable information that is
more reliable for SLAM. To this end, we propose an online framework for sensor
uncertainty estimation that can be trained in a self-supervised manner from
only 2D input data. We further discuss the advantages of the uncertainty
learning for the case of multi-sensor input. Extensive analysis,
experimentation, and ablations show that our proposed modeling paradigm
improves both mapping and tracking accuracy and often performs better than
alternatives that require ground truth depth or 3D. Our experiments show that
we achieve a 38\% and 27\% lower absolute trajectory tracking error (ATE) on
the 7-Scenes and TUM-RGBD datasets respectively. On the popular Replica dataset
using two types of depth sensors, we report an 11\% F1-score improvement on
RGBD SLAM compared to the recent state-of-the-art neural implicit approaches.
Source code: https://github.com/kev-in-ta/UncLe-SLAM.
