通过增强上下文的跨视角定位，实现无GPS无人机高精度自主定位
Precise GPS-Denied UAV Self-Positioning via Context-Enhanced Cross-View Geo-Localization
- 引入动态采样与魔方注意力机制提升特征区分力
- 在密集城市数据集上达到当前最优定位精度
- 适合需要高精度定位的无人机自主导航场景
图像检索已被用作解决无人机（UAV）自主定位挑战的稳健补充技术。然而，现有方法多聚焦于基于部件的复杂表示来定位无人机拍摄的物体，常忽视无人机自身定位的独特挑战，如细粒度空间分辨需求和动态场景变化。为此，我们提出专为无人机自定位设计的上下文增强方法（CEUSP）。该方法结合动态采样策略（DSS）高效选取最优负样本，并通过受魔方旋转机理启发的魔方注意力（RCA）模块，与上下文感知通道融合（CACI）模块协同，增强特征表示与判别能力。大量实验验证了该方法的有效性，在复杂城市环境中显著提升特征表示与定位精度。所提方法在专为密集城市场景设计的DenseUAV数据集上达到顶尖性能，同时在广泛使用的University-1652基准上也表现出竞争力。
原文摘要 · Abstract (English)
Image retrieval has been employed as a robust complementary technique to address the challenge of Unmanned Aerial Vehicles (UAVs) self-positioning. However, most existing methods primarily focus on localizing objects captured by UAVs through complex part-based representations, often overlooking the unique challenges associated with UAV self-positioning, such as fine-grained spatial discrimination requirements and dynamic scene variations. To address the above issues, we propose the Context-Enhanced method for precise UAV Self-Positioning (CEUSP), specifically designed for UAV self-positioning tasks. CEUSP integrates a Dynamic Sampling Strategy (DSS) to efficiently select optimal negative samples, while the Rubik's Cube Attention (RCA) module, combined with the Context-Aware Channel Integration (CACI) module, enhances feature representation and discrimination by exploiting interdimensional interactions, inspired by the rotational mechanics of a Rubik's Cube. Extensive experimental validate the effectiveness of the proposed method, demonstrating notable improvements in feature representation and UAV self-positioning accuracy within complex urban environments. Our approach achieves state-of-the-art performance on the DenseUAV dataset, which is specifically designed for dense urban contexts, and also delivers competitive results on the widely recognized University-1652 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。