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
@article{arxiv.2401.05971,
title = {UAVD4L: A Large-Scale Dataset for UAV 6-DoF Localization},
author = {Rouwan Wu and Xiaoya Cheng and Juelin Zhu and Xuxiang Liu and Maojun Zhang and Shen Yan},
journal= {arXiv preprint arXiv:2401.05971},
year = {2024}
}