无需配对数据，仅用卫星图就能实现无人机精准定位。
Beyond Paired Data: Self-Supervised UAV Geo-Localization from Reference Imagery Alone
- 用卫星图像自监督训练，避开昂贵的无人机-卫星配对数据
- 在真实无人机数据集上达到与有监督方法相当的定位精度
- 适合缺乏标注数据的野外无人机自主导航场景
在无卫星定位信号的环境中，基于图像的定位对无人机自主飞行至关重要。现有先进方法依赖将无人机图像与地理参考的卫星图像匹配，但通常需要大规模配对的无人机-卫星数据集进行训练，这类数据获取成本高且常不可得，限制了实际应用。为解决此问题，我们采用一种新训练范式：训练时不使用无人机图像，直接从卫星视角参考图像中学习。通过专门设计的增强策略模拟卫星视图与真实无人机视图之间的视觉域偏移。我们提出CAEVL模型以支持该范式，并在我们公开的新数据集ViLD（包含真实无人机图像）上进行验证。实验表明，该方法在性能上可与使用配对数据训练的方法相媲美，证明其有效性和强泛化能力。
原文摘要 · Abstract (English)
Image-based localization in GNSS-denied environments is critical for UAV autonomy. Existing state-of-the-art approaches rely on matching UAV images to geo-referenced satellite images; however, they typically require large-scale, paired UAV-satellite datasets for training. Such data are costly to acquire and often unavailable, limiting their applicability. To address this challenge, we adopt a training paradigm that removes the need for UAV imagery during training by learning directly from satellite-view reference images. This is achieved through a dedicated augmentation strategy that simulates the visual domain shift between satellite and real-world UAV views. We introduce CAEVL, an efficient model designed to exploit this paradigm, and validate it on ViLD, a new and challenging dataset of real-world UAV images that we release to the community. Our method achieves competitive performance compared to approaches trained with paired data, demonstrating its effectiveness and strong generalization capabilities.
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