Precise GPS-Denied UAV Self-Positioning via Context-Enhanced Cross-View Geo-Localization

• URL: http://arxiv.org/abs/2502.11408v1
• Date: Mon, 17 Feb 2025 03:49:18 GMT
• Title: Precise GPS-Denied UAV Self-Positioning via Context-Enhanced Cross-View Geo-Localization
• Authors: Yuanze Xu, Ming Dai, Wenxiao Cai, Wankou Yang,
• Abstract summary: 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. Our approach achieves state-of-the-art performance on the DenseUAV dataset, which is specifically designed for dense urban contexts.
• Score: 10.429391988135345
• License:
• Abstract: 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.

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