Self-Supervised Domain Calibration and Uncertainty Estimation for Place Recognition. (arXiv:2203.04446v2 [cs.CV] UPDATED)

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, …

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