update README

README.md

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-license: bsd-3-clause
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+# Multi-Camera Underwater Visual-Inertial Dataset
+
+[![License: BSD-3](https://img.shields.io/badge/License-BSD3-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)
+
+## About
+This repository hosts the **Multi-Camera Underwater Visual-Inertial Dataset** collected by the [Autonomous Robots Lab, NTNU](https://www.autonomousrobotslab.com/).  
+The dataset features data from a variety of onboard sensors, including:
+- 5 cameras
+- 1 IMU (via the [Alphasense Core Research Development Kit](https://github.com/sevensense-robotics/core_research_manual))
+- A barometer
+- Actuator commands from the ROV
+
+Data was collected across both controlled indoor facilities and natural outdoor underwater environments.
+
+---
+
+## Updates / News
+- **April 28, 2025:**  
+  We are uploading new datasets collected in the Trondheim Fjord and at the Marine Cybernetics Lab Pool.  
+  Additionally, we are migrating the dataset repository from [github.com/ntnu-arl/underwater-datasets](https://github.com/ntnu-arl/underwater-datasets) to [huggingface.co/datasets/ntnu-arl/underwater-datasets](https://huggingface.co/datasets/ntnu-arl/underwater-datasets).
+
+---
+
+## Quick Start
+
+You can quickly download the dataset using the `huggingface_hub` Python library.
+
+### 1. Install `huggingface_hub`
+```bash
+pip install huggingface_hub
+```
+
+### 2. Download the Dataset
+You can clone the entire dataset repository using:
+
+```bash
+from huggingface_hub import snapshot_download
+
+# Download the dataset
+snapshot_download(
+    repo_id="ntnu-arl/underwater-datasets",
+    repo_type="dataset",
+    local_dir="underwater-datasets",
+    local_dir_use_symlinks=False  # Set to False to avoid symlinks
+)
+```
+
+This will download the dataset to a local folder called `underwater-datasets/`.
+
+> **Note:** The full dataset is large (many GBs), so ensure you have enough storage and a stable internet connection.
+
+### Alternative: Using `git-lfs`
+If you prefer, you can also clone it manually with `git-lfs`:
+
+```bash
+sudo apt install git-lfs  # Install git lfs
+git lfs install           # Initialize git-lfs
+git clone https://huggingface.co/datasets/ntnu-arl/underwater-datasets   # Clone the dataset
+```
+### 3. Use data with underwater state estimation methods
+You can try out the dataset on the following undewater state estimation methods:
+- ReAqROVIO: Refractive Aquatic ROVIO [code](https://github.com/ntnu-arl/reaqrovio), [homepage](https://ntnu-arl.github.io/refractive-camera-model-in-vio/)
+- DeepVL: Deep Velocity Learning [code](https://github.com/ntnu-arl/DeepVL), [homepage](https://ntnu-arl.github.io/deepvl-deep-velocity-learning/)
+---
+
+## Platform: *Ariel* — Underwater Robot with Multi-Camera, IMU, and Compute Suite
+The data was gathered using a custom-built underwater robot based on the BlueROV2 Heavy Configuration.
+
+<p align="center">
+  <img src="media/robot_exp.png" alt="Ariel Underwater Robot" width="600"/>
+</p>
+
+---
+
+## Trajectories with Visual-Inertial Data
+
+### General Structure
+The dataset is organized into multiple subsets, each corresponding to a different environment.  
+Each subset directory (e.g., `subset-fjord`) contains several trajectory directories (`traj_<X>`), each containing:
+- A `.bag` file with raw sensor data
+- A `.tum` file with the reference trajectory obtained by running [ReAqROVIO](https://github.com/ntnu-arl/reaqrovio) using four cameras and the IMU.
+
+Directory structure overview:
+```
+underwater-datasets/
+├── subset-<environment>/
+│   ├── traj_1/
+│   │   ├── <trajectory>.bag            # Main ROS bag file
+│   │   ├── <trajectory>_baseline.tum    # Reference trajectory (ReAqROVIO output)
+│   ├── traj_2/
+│   │   ├── <trajectory>.bag
+│   │   ├── <trajectory>_baseline.tum
+│   └── ...
+```
+
+---
+
+## Available Subsets
+
+### Subset: Trondheim Fjord (`subset-fjord`)
+This subset includes six trajectories collected by manually piloting *Ariel* in the Trondheim Fjord.
+
+| No. | Length (m) | Duration (s) | Size (GB) |
+|:---:|:----------:|:------------:|:---------:|
+| 1   | 142        | 312          | 12.5      |
+| 2   | 206        | 499          | 23.4      |
+| 3   | 122        | 272          | 13.9      |
+| 4   | 165        | 411          | 23.4      |
+| 5   | 234        | 440          | 18.6      |
+| 6   | 305        | 638          | 26.6      |
+
+---
+
+### Subset: Marine Cybernetics Lab (`subset-mclab`)
+This subset includes two trajectories collected by manually piloting *Ariel* in the Marine Cybernetics Laboratory at NTNU.
+
+| No. | Length (m) | Duration (s) | Size (GB) |
+|:---:|:----------:|:------------:|:---------:|
+| 1   | 135        | 390          | 15.4      |
+| 2   | 125        | 304          | 15.0      |
+
+---
+
+## Reference
+If you use this dataset in your research, please cite the following publication:
+
+> [**An Online Self-Calibrating Refractive Camera Model with Application to Underwater Odometry**](https://ieeexplore.ieee.org/document/10610643)  
+> Mohit Singh, Mihir Dharmadhikari, Kostas Alexis  
+> *IEEE International Conference on Robotics and Automation (ICRA), 2024*
+
+```bibtex
+@INPROCEEDINGS{10610643,
+  author={Singh, Mohit and Dharmadhikari, Mihir and Alexis, Kostas},
+  booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)}, 
+  title={An Online Self-calibrating Refractive Camera Model with Application to Underwater Odometry}, 
+  year={2024},
+  pages={10005-10011},
+  doi={10.1109/ICRA57147.2024.10610643}
+}
+```
+
+
+
+---
+
+## Contact
+For questions or further information, feel free to reach out:
+
+- [Mohit Singh](mailto:[email protected])
+- [Kostas Alexis](mailto:[email protected])