构建首个低空多视角无人机定位基准，提升复杂视图下定位精度。
Exploring the best way for UAV visual localization under Low-altitude Multi-view Observation Condition: a Benchmark
- 构建1.8万张图像的多视角低空数据集AnyVisLoc，含2.5D参考地图。
- 提出统一框架测试现有方法，最优方案达5米内74.1%定位准确率。
- 设计新检索指标PDM@K，更贴合无人机定位任务特性，适合研究者参考。
绝对视觉定位（AVL）使无人机在无卫星信号环境下，通过图像与地理标记参考地图建立几何关系来确定自身位置。尽管已有研究采用图像检索与匹配技术实现AVL，但在低空多视角场景下的研究仍有限。低空多视角条件因视角剧烈变化带来更大挑战。为此，本文构建了一个大规模低空多视角数据集AnyVisLoc，包含18,000张多场景、多高度拍摄的图像，以及融合航拍测绘图与历史卫星图的2.5D参考地图。同时提出统一框架，集成当前先进AVL方法并全面评估性能。以表现最佳组合方法为基线，深入分析影响定位精度的关键因素。该基线在低空多视角条件下实现5米内74.1%的定位准确率。此外，提出新颖检索指标PDM@K，更契合无人机视觉定位任务特征。本基准揭示了低空多视角无人机定位的核心挑战，并为未来研究提供重要指导。数据集与代码已开源。
原文摘要 · Abstract (English)
Absolute Visual Localization (AVL) enables an Unmanned Aerial Vehicle (UAV) to determine its position in GNSS-denied environments by establishing geometric relationships between UAV images and geo-tagged reference maps. While many previous works have achieved AVL with image retrieval and matching techniques, research in low-altitude multi-view scenarios still remains limited. Low-altitude multi-view conditions present greater challenges due to extreme viewpoint changes. To investigate effective UAV AVL approaches under such conditions, we present this benchmark. Firstly, a large-scale low-altitude multi-view dataset called AnyVisLoc was constructed. This dataset includes 18,000 images captured at multiple scenes and altitudes, along with 2.5D reference maps containing aerial photogrammetry maps and historical satellite maps. Secondly, a unified framework was proposed to integrate the state-of-the-art AVL approaches and comprehensively test their performance. The best combined method was chosen as the baseline, and the key factors influencing localization accuracy are thoroughly analyzed based on it. This baseline achieved a 74.1% localization accuracy within 5 m under low-altitude, multi-view conditions. In addition, a novel retrieval metric called PDM@K was introduced to better align with the characteristics of the UAV AVL task. Overall, this benchmark revealed the challenges of low-altitude, multi-view UAV AVL and provided valuable guidance for future research. The dataset and code are available at https://github.com/UAV-AVL/Benchmark
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。