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

翻译：基于深度学习的视觉地点识别技术近年来已成为最先进的方法，但在视觉环境与训练集显著不同的场景下泛化能力不足。因此，为达到最优性能，有时需要对网络进行针对目标环境的微调。为此，我们提出一种自监督域校准流程，该流程基于同时定位与地图构建（SLAM）中的鲁棒位姿图优化作为监督信号，无需依赖GPS或人工标注。此外，我们利用该流程改进地点识别匹配中的不确定性估计，这对安全关键型应用至关重要。实验表明，我们的方法能够在与训练集分布不同的目标环境中提升现有最先进技术的性能，并获取不确定性估计。我们相信，这一方法将有助于从业者在实际应用中部署鲁棒的地点识别解决方案。我们的代码已开源：https://github.com/MISTLab/vpr-calibration-and-uncertainty