Towards UAV Localization in GNSS-Denied Environments: The SatLoc Dataset and a Hierarchical Adaptive Fusion Framework
Xiang Zhou, Xiangkai Zhang, Yang Xu, Jiannan Zhao, Zhiyong Liu, Feng Shuang
Abstract
Precise and robust localization for micro Unmanned Aerial Vehicles (UAVs) in GNSS-denied environments is hindered by the lack of diverse datasets and the limited real-world performance of existing visual matching methods. To address these gaps, we introduce two contributions: (1) the SatLoc dataset, a new benchmark featuring synchronized, multi-source data from varied real-world scenarios tailored for UAV-to-satellite image matching, and (2) SatLoc-Fusion, a hierarchical localization framework. Our proposed pipeline integrates three complementary layers: absolute geo-localization via satellite imagery using DinoV2, high-frequency relative motion tracking from visual odometry with XFeat, and velocity estimation using optical flow. An adaptive fusion strategy dynamically weights the output of each layer based on real-time confidence metrics, ensuring an accurate and self-consistent state estimate. Deployed on a 6 TFLOPS edge computer, our system achieves real-time operation at over 2 Hz, with an absolute localization error below 15 m and effective trajectory coverage exceeding 90%, demonstrating state-of-the-art performance. The SatLoc dataset and fusion pipeline provide a robust and comprehensive baseline for advancing UAV navigation in challenging environments.