UAVD4L: A Large-Scale Dataset for UAV 6-DoF Localization

• URL: http://arxiv.org/abs/2401.05971v1
• Date: Thu, 11 Jan 2024 15:19:21 GMT
• Title: UAVD4L: A Large-Scale Dataset for UAV 6-DoF Localization
• Authors: Rouwan Wu, Xiaoya Cheng, Juelin Zhu, Xuxiang Liu, Maojun Zhang, Shen Yan
• Abstract summary: We introduce a large-scale 6-DoF UAV dataset for localization (UAVD4L) We develop a two-stage 6-DoF localization pipeline (UAVLoc), which consists of offline synthetic data generation and online visual localization. Results on the new dataset demonstrate the effectiveness of the proposed approach.
• Score: 14.87295056434887
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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