论文标题
自我监督的域校准和位置识别的不确定性估计
Self-Supervised Domain Calibration and Uncertainty Estimation for Place Recognition
论文作者
论文摘要
基于深度学习的视觉位置识别技术近年来将自己作为最先进的技术，并不能很好地概括与训练集在视觉上不同的环境。因此，为了达到最佳性能，有时有必要将网络调整到目标环境中。为此，我们基于同时定位和映射（SLAM）作为监督信号而不需要GPS或手动标记的，基于同时定位和映射（SLAM）的强大姿势图优化的自我监管域校准程序。此外，我们利用该程序来改善对位置识别匹配的不确定性估计，这在安全关键应用中很重要。我们表明，我们的方法可以提高目标环境与训练集不同的最先进技术的性能，并且我们可以获得不确定性估计。我们认为，这种方法将帮助从业者在现实世界应用中部署健壮的位置识别解决方案。我们的代码可公开使用：https：//github.com/mistlab/vpr-calibration-and-uncriblation--uncrightity
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
