Game4Loc：一项基于游戏数据的无人机地理定位基准测试

Game4Loc: A UAV Geo-Localization Benchmark from Game Data

September 25, 2024

作者: Yuxiang Ji, Boyong He, Zhuoyue Tan, Liaoni Wu

cs.AI

摘要

基于视觉的 无人机 地理定位技术，作为全球导航卫星系统（GNSS）之外的 GPS 信息的辅助来源，仍然可以在无 GPS 环境中独立运行。最近基于深度学习的方法将其归因为图像匹配和检索任务。通过在地理标记的卫星图像数据库中检索 无人机 视角图像，可以获得近似的定位信息。然而，由于高昂的成本和隐私问题，通常很难获得大量连续区域的 无人机 视角图像。现有的 无人机 视角数据集主要由小规模航拍组成，强烈假设对于任何查询都存在一个完美的一对一对齐参考图像，这与实际定位场景存在显著差距。在这项工作中，我们构建了一个名为 GTA-UAV 的大范围连续区域 无人机 地理定位数据集，利用现代电脑游戏展示多个飞行高度、姿态、场景和目标。基于该数据集，我们引入了一个更实际的 无人机 地理定位任务，包括跨视图配对数据的部分匹配，并将图像级的检索扩展到实际距离（米）上的定位。为了构建 无人机 视角和卫星视角对，我们采用基于权重的对比学习方法，这样可以在避免额外后处理匹配步骤的同时实现有效学习。实验证明了我们的数据和训练方法对于 无人机 地理定位的有效性，以及对真实场景的泛化能力。

English

The vision-based geo-localization technology for UAV, serving as a secondary source of GPS information in addition to the global navigation satellite systems (GNSS), can still operate independently in the GPS-denied environment. Recent deep learning based methods attribute this as the task of image matching and retrieval. By retrieving drone-view images in geo-tagged satellite image database, approximate localization information can be obtained. However, due to high costs and privacy concerns, it is usually difficult to obtain large quantities of drone-view images from a continuous area. Existing drone-view datasets are mostly composed of small-scale aerial photography with a strong assumption that there exists a perfect one-to-one aligned reference image for any query, leaving a significant gap from the practical localization scenario. In this work, we construct a large-range contiguous area UAV geo-localization dataset named GTA-UAV, featuring multiple flight altitudes, attitudes, scenes, and targets using modern computer games. Based on this dataset, we introduce a more practical UAV geo-localization task including partial matches of cross-view paired data, and expand the image-level retrieval to the actual localization in terms of distance (meters). For the construction of drone-view and satellite-view pairs, we adopt a weight-based contrastive learning approach, which allows for effective learning while avoiding additional post-processing matching steps. Experiments demonstrate the effectiveness of our data and training method for UAV geo-localization, as well as the generalization capabilities to real-world scenarios.