NTU VIRAL: A Visual-Inertial-Ranging-Lidar Dataset for Autonomous Aerial Vehicle
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.
Citation
If you use some resource from this data suite, please cite it as
@article{nguyen2022ntu,
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 = {The International Journal of Robotics Research},
volume = {41},
number = {3},
pages = {270--280},
year = {2022},
publisher = {SAGE Publications Sage UK: London, England}
}
Updates
05/02/2023: Update works using the datasets
26/09/2022: Extra sequences (rtp_01, rtp_02, rtp_03, tnp_01, tnp_02, tnp_03, spms_01, spms_02, spms_03) in more challenging scenarios are added.
12/07/2022: The ouster pointcloud and IMU messages are found to jitter due to synchronization issues. A script to regularize the ouster pointcloud and imu topics can be downloaded here.
Downloads
Note: The files below are hosted on NTU Data Repository. If you experience interuption from the NTU Data Reposity, please try downloading the files from this Onedrive folder.
Groundtruth is included in the bag. Or you can download them in csv formats here: https://github.com/ntu-aris/ntuviral_gt.
We note that many users forget to check for the 0.4m offset from the IMU to the prism, which is where ground truth is measured. To help evaluating things more easily, we have written a jupyter and MATLAB scripts to help with the evalution. Please check out the tutorial for their use.
| Name | Link | Size | Duration | Remark |
|---|---|---|---|---|
| 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 inside the Nanyang Auditorium |
| nya_02 | .zip | 9.4 GB | 428.7 s | Collected inside the Nanyang Auditorium |
| nya_03 | .zip | 9.0 GB | 411.2 s | Collected inside the Nanyang Auditorium |
| sbs_01 | .zip | 7.8 GB | 354.2 s | Collected at the School of Bio. Science's front square |
| sbs_02 | .zip | 8.2 GB | 373.3 s | Collected at the School of Bio. Science's front square |
| sbs_03 | .zip | 8.5 GB | 389.3 s | Collected at the School of Bio. Science's front square |
| rtp_01 | .zip | 5.0 GB | 482 s | Collected at the Research Techno Plaza's carpark |
| rtp_02 | .zip | 5.2 GB | 439 s | Collected at the Research Techno Plaza's carpark |
| rtp_03 | .zip | 4.0 GB | 355 s | Collected at the Research Techno Plaza's carpark |
| tnp_01 | .zip | 8.1 GB | 583 s | Collected inside Research Techno Plaza |
| tnp_02 | .zip | 6.2 GB | 457 s | Collected inside Research Techno Plaza |
| tnp_03 | .zip | 5.5 GB | 407 s | Collected inside Research Techno Plaza |
| spms_01 | .zip | 5.5 GB | 446 s | School of Physical and Mathematical Science's Facade |
| spms_02 | .zip | 4.0 GB | 398 s | School of Physical and Mathematical Science's Facade |
| spms_03 | .zip | 5.0 GB | 386 s | School of Physical and Mathematical Science's Facade |
| 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 |
Quick use
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.
| Method | Repository | Credit |
|---|---|---|
| Open-VINS | https://github.com/brytsknguyen/open_vins | Forked from https://github.com/rpng/open_vins |
| VINS-Fusion | https://github.com/brytsknguyen/VINS-Fusion | Forked from https://github.com/HKUST-Aerial-Robotics/VINS-Fusion |
| VINS-Mono | https://github.com/brytsknguyen/VINS-Mono | Forked from https://github.com/HKUST-Aerial-Robotics/VINS-Mono |
| M-LOAM | https://github.com/brytsknguyen/M-LOAM | Forked from https://github.com/gogojjh/M-LOAM |
| LIO-SAM | https://github.com/brytsknguyen/LIO-SAM | Forked from https://github.com/TixiaoShan/LIO-SAM |
| A-LOAM | https://github.com/brytsknguyen/A-LOAM | Forked from https://github.com/HKUST-Aerial-Robotics/A-LOAM |
| FAST-LIO | https://github.com/brytsknguyen/FAST_LIO | Forked from MARS Lab, HKU |
| FAST-LIVO | https://github.com/hku-mars/FAST-LIVO | MARS Lab, HKU |
| SLICT | https://github.com/brytsknguyen/SLICT | An NTU-KTH collaboration via Wallenberg-NTU Postdoctoral Fellowship |
| CLIC | https://github.com/brytsknguyen/clic | Forked from https://github.com/APRIL-ZJU/clic |
Related works
The datasets were used in the following papers. Please checkout these works if you are interested. (Please contact us if you would like your work mentioned here).
- FAST-LIVO: Fast and Tightly-coupled Sparse-Direct LiDAR-Inertial-Visual Odometry
- 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)
Notes:
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.
Licence
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.