Unmanned Aerial Vehicles (UAVs) rely on satellite systems for stable positioning. However, due to limited satellite coverage or communication disruptions, UAVs may lose signals from satellite-based positioning systems. In such situations, vision-based techniques can serve as an alternative, ensuring the self-positioning capability of UAVs. However, most of the existing datasets are developed for the geo-localization tasks of the objects identified by UAVs, rather than the self-positioning task of UAVs. Furthermore, the current UAV datasets use discrete sampling on synthetic data, such as Google Maps, thereby neglecting the crucial aspects of dense sampling and the uncertainties commonly experienced in real-world scenarios. To address these issues, this paper presents a new dataset, DenseUAV, which is the first publicly available dataset designed for the UAV self-positioning task. DenseUAV adopts dense sampling on UAV images obtained in low-altitude urban settings. In total, over 27K UAV-view and satellite-view images of 14 university campuses are collected and annotated, establishing a new benchmark. In terms of model development, we first verify the superiority of Transformers over CNNs in this task. Then, we incorporate metric learning into representation learning to enhance the discriminative capacity of the model and to lessen the modality discrepancy. Besides, to facilitate joint learning from both perspectives, we propose a mutually supervised learning approach. Last, we enhance the Recall@K metric and introduce a new measurement, SDM@K, to evaluate the performance of a trained model from both the retrieval and localization perspectives simultaneously. As a result, the proposed baseline method achieves a remarkable Recall@1 score of 83.05% and an SDM@1 score of 86.24% on DenseUAV. The dataset and code will be made publicly available on https://github.com/Dmmm1997/DenseUAV.

翻译：无人机依赖卫星系统实现稳定定位。然而，由于卫星覆盖范围有限或通信中断，无人机可能丢失卫星定位系统信号。在此类场景中，基于视觉的技术可作为替代方案，确保无人机的自定位能力。然而，现有数据集大多面向无人机识别目标的定位任务，而非无人机自身定位任务。此外，当前无人机数据集采用对合成数据（如谷歌地图）的离散采样方式，忽略了密集采样以及真实场景中常见的不确定性等关键因素。针对上述问题，本文提出新数据集DenseUAV——这是首个公开面向无人机自定位任务的数据集。DenseUAV对低空城市环境中获取的无人机图像进行密集采样，共收集并标注了14个大学校园的超过2.7万张无人机视角与卫星视角图像，构建了新的基准测试。在模型开发方面，我们首先验证了Transformer在该任务中优于CNN的性能。随后将度量学习融入表征学习，以增强模型的判别能力并减小模态差异。此外，为促进两种视角的联合学习，我们提出互监督学习方法。最后，我们改进了Recall@K指标并引入新度量SDM@K，从检索与定位双重视角同时评估训练模型性能。实验结果表明，所提出的基准方法在DenseUAV数据集上取得了83.05%的Recall@1得分与86.24%的SDM@1得分。该数据集与代码将在https://github.com/Dmmm1997/DenseUAV 公开提供。