.. MUN-FRL Dataset documentation master file, created by sphinx-quickstart on Sat Oct 15 14:42:58 2022. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. MUN-FRL: Aerial Visual-Inertial-LiDAR Odometry and Mapping Dataset =================================================================== .. image:: /images/pl1.jpg :width: 23% .. image:: /images/pl_v1.png :width: 35% .. image:: /images/LH_m600_flight_cp.png :width: 39% .. image:: /images/bell_1_map.jpg :width: 98% 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 :doc:`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 :ref:`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 --------- .. csv-table:: :header: Dataset,Size [GB],Length [m],Duration [s],ROS bag, PPK file, FRL file :widths: 20,10,10,10,10,10,10 quarry1,27.2,357,231, `link `_ , `link `_ , N/A quarry2,79.8,807,675, `link `_ , `link `_ , N/A lighthouse,89.9,890,756, `link `_ , `link `_ , N/A bell412_dataset1,45.9,1709,432, `link `_ , `link `_ , `link `_ bell412_dataset2,45.8,x,436, `link `_ , `link `_ , `link `_ bell412_dataset3,32.2,4336,308, `link `_ , `link `_ , `link `_ bell412_dataset4,33.0,3656,316, `link `_ , `link `_ , `link `_ bell412_dataset5,34.1,2138,483, `link `_ , `link `_ , `link `_ bell412_dataset6,47.6,4938,520, `link `_ , `WIP `_ , `link `_ Quick Tests on State-of-the-art Algorithms ------------------------------------------ .. csv-table:: :header: Algorithm, Git repo with munfrl launch files :widths: 10,30 VINS-Fusion , https://github.com/sendtooscar/VINS-Fusion-gpu FAST-LIO2 , https://github.com/sendtooscar/FAST_LIO.git ALOAM , https://github.com/sendtooscar/A-LOAM SVO2 , https://github.com/sendtooscar/SVO2 FAST-LVIO2 , work-in-progres CCECE 2025 Workshop Material ------------------------------------------ .. csv-table:: :header: Type, Description, Size, Download Link :widths: 10,30,10,10 Data bag,Bell412_dataset1_benchmarking_bag, 6.00 GB, `link `_ Data bag, Lighthouse_benchmarking_bag, 3.55 GB, `link `_ Foxglove layout, Fox glove raw data visualization layout, 5 KB, `link `_ Foxglove layout, Fox glove processed data visualization layout, 15 KB, `link `_ Colab Notebook, Landing zone tutorial notebook link (Google Colab), online, `link `_ Colab Notebook, Radar Super resolution tutorial notebook link (Google Colab), online, `link `_ Colab Notebook, GCS trajectory optimization tutorial notebook link (Google Colab), online, `link `_ 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: .. code-block:: bibtex @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} } .. toctree:: :maxdepth: 2 :hidden: :caption: Dataset Overview sensors Calibration ground truth