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

TL;DR

This paper introduces CEUSP, a novel method for UAV self-positioning that leverages context-aware feature enhancement and dynamic sampling to improve accuracy in urban environments, outperforming existing techniques.

Contribution

The paper presents a new UAV self-positioning approach combining a dynamic sampling strategy with a Rubik's Cube-inspired attention module for better feature discrimination.

Findings

Achieves state-of-the-art accuracy on DenseUAV dataset.

Demonstrates significant improvements over existing methods.

Effective in complex urban scenes.

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.