This site presents the datasets collected from our research Unmanned Aerial Vehicle (UAV) platform, featuring an extensive set of sensors:
- Two 3D lidars
- Two time-synchronized cameras
- Multiple Inertial Measurement Units (IMUs)
- Four Ultra-wideband (UWB) nodes on UAV, ranging to three anchor nodes.
The comprehensive sensor suite resembles that of an autonomous driving car, but features distinct and challenging characteristics of aerial operations. The flight tests are conducted in a variety of both indoor and outdoor conditions.
If you use some resource from this data suite, please cite it as
@article{nguyen2021ntuviral,
title={NTU VIRAL: A Visual-Inertial-Ranging-Lidar dataset, from an aerial vehicle viewpoint},
author={Nguyen, Thien-Minh and Yuan, Shenghai and Cao, Muqing and Lyu, Yang and Nguyen, Thien Hoang and Xie, Lihua},
journal={Internationl Journal of Robotics Research (accepted, to appear)},
year={2021}
}
[PDF]
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|---|---|---|---|---|
| eee_01 | .zip | 8.7 GB | 398.7 s | Collected at the School of EEE central carpark |
| eee_02 | .zip | 7.0 GB | 321.1 s | Collected at the School of EEE central carpark |
| eee_03 | .zip | 4.3 GB | 181.4 s | Collected at the School of EEE central carpark |
| nya_01 | .zip | 8.6 GB | 396.3 s | Collected at the School of Bio. Science's front square |
| nya_02 | .zip | 9.4 GB | 428.7 s | Collected at the School of Bio. Science's front square |
| nya_03 | .zip | 9.0 GB | 411.2 s | Collected at the School of Bio. Science's front square |
| sbs_01 | .zip | 7.8 GB | 354.2 s | Collected inside the Nanyang Auditorium |
| sbs_02 | .zip | 8.2 GB | 373.3 s | Collected inside the Nanyang Auditorium |
| sbs_03 | .zip | 8.5 GB | 389.3 s | Collected inside the Nanyang Auditorium |
| calib_stereo | .zip | 49 MB | - | Image pairs for intrinsic calibration |
| calib_stereo_imu | .bag | 0.96 GB | 131.7 s | Bag file for stereo camera - IMU calibration using Kalibr |
(Alternatively, the files above can be downloaded from our NTU Data Repository page.)
We have done some experiments of state-of-the-art methods on our the datasets. If you are seeking to do the same, please check out the following to get the work done quickly.
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|---|---|
| Open-VINS | https://github.com/brytsknguyen/open_vins |
| VINS-Fusion | https://github.com/brytsknguyen/VINS-Fusion |
| VINS-Mono | https://github.com/brytsknguyen/VINS-Mono |
| M-LOAM | https://github.com/brytsknguyen/M-LOAM |
| LIO-SAM | https://github.com/brytsknguyen/LIO-SAM |
| A-LOAM | https://github.com/brytsknguyen/A-LOAM |
The datasets were used in the following papers. Please checkout these works if you are interested.
- VIRAL SLAM: Tightly Coupled Camera-IMU-UWB-Lidar SLAM
- MILIOM: Tightly Coupled Multi-Input Lidar-Inertia Odometry and Mapping (RAL 2021)
- LIRO: Tightly Coupled Lidar-Inertia-Ranging Odometry (ICRA 2021)
For more information on the sensors and how to use the dataset, please checkout the other sections.
For resources and other works of our group please checkout our github.
If you have some inquiry, please raise an issue on github.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License and is intended for non-commercial academic use. If you are interested in using the dataset for commercial purposes please contact us.