DiffusionUavLoc: Visually Prompted Diffusion for Cross-View UAV Localization
This addresses localization for UAVs in intelligent patrol systems where satellite signals are unavailable, though it appears incremental as it builds on existing cross-view retrieval methods.
The paper tackles cross-view UAV localization in GNSS-denied environments by proposing DiffusionUavLoc, a framework that uses visually prompted diffusion to bridge geometric and appearance gaps between UAV and satellite images, achieving competitive performance on datasets like University-1652 and SUES-200.
With the rapid growth of the low-altitude economy, unmanned aerial vehicles (UAVs) have become key platforms for measurement and tracking in intelligent patrol systems. However, in GNSS-denied environments, localization schemes that rely solely on satellite signals are prone to failure. Cross-view image retrieval-based localization is a promising alternative, yet substantial geometric and appearance domain gaps exist between oblique UAV views and nadir satellite orthophotos. Moreover, conventional approaches often depend on complex network architectures, text prompts, or large amounts of annotation, which hinders generalization. To address these issues, we propose DiffusionUavLoc, a cross-view localization framework that is image-prompted, text-free, diffusion-centric, and employs a VAE for unified representation. We first use training-free geometric rendering to synthesize pseudo-satellite images from UAV imagery as structural prompts. We then design a text-free conditional diffusion model that fuses multimodal structural cues to learn features robust to viewpoint changes. At inference, descriptors are computed at a fixed time step t and compared using cosine similarity. On University-1652 and SUES-200, the method performs competitively for cross-view localization, especially for satellite-to-drone in University-1652.Our data and code will be published at the following URL: https://github.com/liutao23/DiffusionUavLoc.git.