RflyPano: A Panoramic Benchmark for Ultra-low Altitude UAV Localization Powered by RflySim

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Authors: Association for Artificial Intelligence 2026, Dun Dai, Xunhua Dai, Ze Lu, Quan Quan
Cited by: 0

Abstract

Ultra-low altitude UAVs (below 120 meters) are gaining importance in the booming low-altitude economy, where GNSS signals are often unreliable or unavailable. Vision-based localization emerges as a promising alternative; however, existing benchmarks are not designed for ultra-low flight and typically adopt pinhole cameras with limited field of view, making them less effective in handling occlusions and repetitive textures near the ground. To address these limitations, we introduce the first panoramic UAV localization dataset tailored for ultra-low altitude scenarios. Built on a four-fisheye-camera system in the high-fidelity RflySim platform, our dataset captures diverse conditions — including day/night cycles, extreme weather, and dynamic obstacles — and contains over hundreds of thousands of frames. It is further enhanced with real-world UAV panoramic data to narrow the sim-to-real gap and will be continuously updated for broader applicability. Comprehensive experiments confirm the effectiveness and transferability of our dataset, establishing it as a robust benchmark for future research in vision-based UAV localization.

Fields of Study & Subjects

Robotics and Sensor-Based Localization Aerospace Engineering and Control Systems 3D Surveying and Cultural Heritage

Publication details

Journal / source
Underline Science Inc.
Publication date
7 January 2026
Language
EN