MuRDE-FPN: Precise UAV Localization Using Enhanced Feature Pyramid Network

Unmanned aerial vehicles require reliable autonomous positioning beyond the limitations of GNSS, motivating the development of a vision-based, end-to-end Finding Point in Map algorithm. This study introduces MuRDE-FPN, an enhanced Feature Pyramid Network (FPN) designed for precise UAV localization, building upon a lightweight one-stream OS-PCPVT transformer backbone. MuRDE-FPN integrates Efficient Channel Attention for adaptive channel recalibration and features two novel components: a MultiReceptive Deformable Enhancement block that utilizes DCNv2 with varying kernel sizes to refine the semantically rich final feature layer, and a Feature Alignment Module for robust layer merging. Evaluated on the UL14 dataset and a new, more diverse UAV-Sat dataset (derived from UAV-VisLoc), MuRDE-FPN consistently outperformed 5 state-of-the-art FPI methods (FPI, WAMF-FPI, OS-FPI, DCD-FPI). It achieved an RDS of 84.26 on UL14 and 63.74 on UAV-Sat, demonstrating superior localization precision. Ablation studies confirmed the cumulative benefits of ECA, MuRDE, and FAM. These findings highlight the effectiveness of custom FPN designs and targeted feature enhancements for UAV-Satellite precise positioning, with MuRDE-FPN providing a robust solution and the UAV-Sat dataset offering a new benchmark for evaluation. Future efforts will address computational efficiency and performance across varying data-quality environments.