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Visual place recognition techniques based on deep learning, which have imposed themselves as the state-of-the-art in recent years, do not always generalize well to environments that are 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 completely self-supervised domain calibration procedure based on robust pose graph estimation from Simultaneous Localization andarXiv:2203.04446v1 fatcat:oiwe5iuj65c6jjtl53sfz2qssm