MUN-FRL: Aerial Visual-Inertial-LiDAR Odometry and Mapping Dataset
This webpage presents the visual-inertial-LiDAR (VIL) datasets collected by an interchagable payload unit atttached to a Bell 412 Advanced Systems Research Helicopter (ASRA) helicoptor and a DJI M600 hexacoptor drone. The payload unit consists of two monocular/RGB global shutter cameras, a 3D LiDAR, an IMU, real-time kinematic (RTK) enabled global navigation system (GNSS) receiver and a Jetson AGX Xavier GPU as the processing unit. The two cameras, IMU, LiDAR and GNSS receivers are hardware time-synchronized.
Latest Updates
[March 2026] New Calibration Data: Added intrinsic and extrinsic calibration datasets for the Bell 412 data sequences. See the Sensor Calibration page.
[March 2026] License Update: The dataset files are now explicitly licensed under CC BY 4.0 to facilitate both academic and commercial use. See Lisence.
Available Data
Images
FLIR BFS-U3-16S2M-BD - Nadir view [global shutter, 1140x1080, monochrome (Mono8) and color (RGB8), 20Hz]
FLIR BFS-PGE-04S2C-CS - Forward view [global shutter, 720x540, monochrome (Mono8) and color (RGB8), 20Hz]
IMU Measurements
Xsens MTi-30 IMU - [angular rate, accleration - 400Hz, magnetic field - 100Hz]
Pointclouds
Velodyne VLP-16 LiDAR - Downward facing [pointclouds, 360° horizontal, 30° vertical FOV, 10Hz]
Ground-Truth
simpleRTK2B RTK-GNSS receriver - [3D position, 5Hz]
Post processed kenematic (PPK) ground truth - [3D position, 5Hz]
Downloads
Dataset |
Size [GB] |
Length [m] |
Duration [s] |
ROS bag |
PPK file |
FRL file |
|---|---|---|---|---|---|---|
quarry1 |
27.2 |
357 |
231 |
N/A |
||
quarry2 |
79.8 |
807 |
675 |
N/A |
||
lighthouse |
89.9 |
890 |
756 |
N/A |
||
bell412_dataset1 |
45.9 |
1709 |
432 |
|||
bell412_dataset2 |
45.8 |
x |
436 |
|||
bell412_dataset3 |
32.2 |
4336 |
308 |
|||
bell412_dataset4 |
33.0 |
3656 |
316 |
|||
bell412_dataset5 |
34.1 |
2138 |
483 |
|||
bell412_dataset6 |
47.6 |
4938 |
520 |
Quick Tests on State-of-the-art Algorithms
Algorithm |
Git repo with munfrl launch files |
|---|---|
VINS-Fusion |
|
FAST-LIO2 |
|
ALOAM |
|
SVO2 |
|
FAST-LVIO2 |
work-in-progres |
CCECE 2025 Workshop Material
Type |
Description |
Size |
Download Link |
|---|---|---|---|
Data bag |
Bell412_dataset1_benchmarking_bag |
6.00 GB |
|
Data bag |
Lighthouse_benchmarking_bag |
3.55 GB |
|
Foxglove layout |
Fox glove raw data visualization layout |
5 KB |
|
Foxglove layout |
Fox glove processed data visualization layout |
15 KB |
|
Colab Notebook |
Landing zone tutorial notebook link (Google Colab) |
online |
|
Colab Notebook |
Radar Super resolution tutorial notebook link (Google Colab) |
online |
|
Colab Notebook |
GCS trajectory optimization tutorial notebook link (Google Colab) |
online |
Lisence
The MUN-FRL Dataset (including all LiDAR pointclouds, GPS/IMU logs, and RGB images) is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License.
Note
While the IJRR publication is distributed under a Non-Commercial (CC BY-NC 4.0) license, the underlying raw data and calibration files provided here are explicitly licensed under CC BY 4.0 to support both academic and commercial research in aerial autonomy.
Under this license, you are free to: * Share — copy and redistribute the material in any medium or format. * Adapt — remix, transform, and build upon the material for any purpose, even commercially.
The only requirement is that you give appropriate credit by citing the original paper.
Citation
If you use this dataset or the associated tools in your research, please cite the following publication:
@article{thalagala2024munfrl,
title={MUN-FRL: A Visual-Inertial-LiDAR Dataset for Aerial Autonomous Navigation and Mapping},
author={Thalagala, Ravindu G and De Silva, Oscar, Mann, George KI and Gosine, Raymond G},
journal={The International Journal of Robotics Research},
volume={43},
number={12},
pages={1853--1866},
year={2024},
publisher={SAGE Publications},
doi={10.1177/02783649241238318}
}