超越瓷砖匹配：连接不一致的航空和卫星视图以实现仅视觉UAV导航
Beyond Matching to Tiles: Bridging Unaligned Aerial and Satellite Views for Vision-Only UAV Navigation
AI总结：
本文提出Bearing-UAV方法，通过联合预测UAV绝对位置和航向实现跨视图导航，提升在复杂环境下的定位精度和鲁棒性。
摘要：
最近的跨视图地理定位（CVGL）方法在支持GNSS拒止环境下的无人机导航中展现出强大潜力。然而，现有方法主要关注将无人机视图匹配到机载地图瓷砖，导致准确性和存储开销之间的权衡，并忽视了导航过程中无人机航向的重要性。此外，跨视图场景中的显著差异和变化的重叠程度未被充分考虑，限制了其在现实场景中的泛化能力。本文提出了Bearing-UAV，一种纯视觉驱动的跨视图导航方法，通过联合预测邻近特征以获得UAV的绝对位置和航向，实现野外准确、轻量且鲁棒的导航。该方法利用全局和局部结构特征，并显式编码相对空间关系，使其对跨视图变化、不一致性和特征稀疏条件具有鲁棒性。我们还提出了Bearing-UAV-90k，一个多城市基准用于评估跨视图定位和导航。大量实验表明，Bearing-UAV在多样地形上比先前的匹配/检索范式具有更低的定位误差。我们的代码和数据集将公开发布。
英文摘要：
Recent advances in cross-view geo-localization (CVGL) methods have shown strong potential for supporting unmanned aerial vehicle (UAV) navigation in GNSS-denied environments. However, existing work predominantly focuses on matching UAV views to onboard map tiles, which introduces an inherent trade-off between accuracy and storage overhead, and overlooks the importance of the UAV's heading during navigation. Moreover, the substantial discrepancies and varying overlaps in cross-view scenarios have been insufficiently considered, limiting their generalization to real-world scenarios. In this paper, we present Bearing-UAV, a purely vision-driven cross-view navigation method that jointly predicts UAV absolute location and heading from neighboring features, enabling accurate, lightweight, and robust navigation in the wild. Our method leverages global and local structural features and explicitly encodes relative spatial relationships, making it robust to cross-view variations, misalignment, and feature-sparse conditions. We also present Bearing-UAV-90k, a multi-city benchmark for evaluating cross-view localization and navigation. Extensive experiments show encouraging results that Bearing-UAV yields lower localization error than previous matching/retrieval paradigm across diverse terrains. Our code and dataset will be made publicly available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
