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}
}

[Journal][Preprint]

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.

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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.

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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.