Tune your Place Recognition: Self-Supervised Domain Calibration via Robust SLAM [article]

Pierre-Yves Lajoie, Giovanni Beltrame
2022 arXiv   pre-print
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 and
more » ... ing (SLAM) as the supervision signal without requiring GPS or manual labeling. We first show that the training samples produced by our technique are sufficient to train a visual place recognition system from a pre-trained classification model. Then, we show that our approach can improve the performance of a state-of-the-art technique on a target environment dissimilar from the training set. We believe that this approach will help practitioners to deploy more robust place recognition solutions in real-world applications.
arXiv:2203.04446v1 fatcat:oiwe5iuj65c6jjtl53sfz2qssm