  Visual place recognition techniques based on deep learning, which have
imposed themselves as the state-of-the-art in recent years, do not generalize
well to environments visually different from the training set. Thus, to achieve
top performance, it is sometimes necessary to fine-tune the networks to the
target environment. To this end, we propose a self-supervised domain
calibration procedure based on robust pose graph optimization from Simultaneous
Localization and Mapping (SLAM) as the supervision signal without requiring GPS
or manual labeling. Moreover, we leverage the procedure to improve uncertainty
estimation for place recognition matches which is important in safety critical
applications. We show that our approach can improve the performance of a
state-of-the-art technique on a target environment dissimilar from its training
set and that we can obtain uncertainty estimates. We believe that this approach
will help practitioners to deploy robust place recognition solutions in
real-world applications. Our code is available publicly:
https://github.com/MISTLab/vpr-calibration-and-uncertainty
