UAVD4L: A Large-Scale Dataset for UAV 6-DoF Localization
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916088507269120 |
|---|---|
| author | Wu, Rouwan Cheng, Xiaoya Zhu, Juelin Liu, Xuxiang Zhang, Maojun Yan, Shen |
| author_facet | Wu, Rouwan Cheng, Xiaoya Zhu, Juelin Liu, Xuxiang Zhang, Maojun Yan, Shen |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05971 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | UAVD4L: A Large-Scale Dataset for UAV 6-DoF Localization Wu, Rouwan Cheng, Xiaoya Zhu, Juelin Liu, Xuxiang Zhang, Maojun Yan, Shen Computer Vision and Pattern Recognition 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 |
| title | UAVD4L: A Large-Scale Dataset for UAV 6-DoF Localization |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.05971 |