UAVD4L: A Large-Scale Dataset for UAV 6-DoF Localization
Rouwan Wu Xiaoya Cheng Juelin Zhu Xuxiang Liu Maojun Zhang Shen Yan
Despite significant progress in global localization of Unmanned Aerial
Vehicles (UAVs) in GPS-denied environments, existing methods remain constrained
by the availability of datasets. Current datasets often focus on small-scale
scenes and lack viewpoint variability, accurate ground truth (GT) pose, and UAV
build-in sensor data. To address these limitations, we introduce a large-scale
6-DoF UAV dataset for localization (UAVD4L) and develop a two-stage 6-DoF
localization pipeline (UAVLoc), which consists of offline synthetic data
generation and online visual localization. Additionally, based on the 6-DoF
estimator, we design a hierarchical system for tracking ground target in 3D
space. Experimental results on the new dataset demonstrate the effectiveness of
the proposed approach. Code and dataset are available at
https://github.com/RingoWRW/UAVD4L