AURORA, A multi sensor dataset for robotic ocean exploration

This repository accompanies the dataset and the report "AURORA, A multi sensor dataset for robotic ocean exploration", by Marco Bernardi, Brett Hosking, Chiara Petrioli, Brian J. Bett, Daniel Jones, Veerle Huvenne, Rachel Marlow, Maaten Furlong, Steve McPhail and Andrea Munafo.

Abstract The current maturity of autonomous underwater vehicles (AUVs) has made their deployment practical and cost-effective, such that many scientific, industrial and military applications now include AUV operations. However, the logistical difficulties and high costs of operating at-sea are still critical limiting factors in further technology development, the benchmarking of new techniques and the reproducibility of research results. To overcome this problem, we present a freely available dataset suitable to test control, navigation, sensor processing algorithms and others tasks. This dataset combines AUV navigation data, side-scan sonar, multibeam echosounder data and seafloor camera image data, and associated sensor acquisition meta-data to provide a detailed characterisation of surveys carried out by the National Oceanography Centre (NOC) in the Greater Haig Fras Marine Conservazion Zone (MCZ) of the U.K in 2015.

Packages and dependencies

Necessary packages:

This notebook depends on the following packages:

• scipy pip install scipy
• GDAL pip install GDAL
• OpenCV pip install opencv-python
• Pillow pip install Pillow
• pyxtf pip3 install pyxtf
• ipyleaflet pip install ipyleaflet

The repository also forks the pyall package, and a slightly modified version is provided in the utils folder.

There is a conda equivalent for some of the packages and you might decide to use that:

• conda install -c anaconda scipy
• conda install -c conda-forge gdal
• conda install -c conda-forge ipyleaflet

You can also create an anaconda environment using the req.txt file provided in the env diredctory of this repository:

conda env create --file env/req.txt

or through the environment file environment.yml provided in the same folder:

conda env create --file env/environment.yml

To integrate it better with jupyter notebook run the following command:

python -m ipykernel install --user --name aurora --display-name="aurora"

You can now select the correct kernel directly from the Kernel menu.

If your jupyter_client in your environment is <5.3 then you might need to activate the ipyleaflet environment. You can do so, running:

jupyter nbextension enable --py --sys-prefix ipyleaflet

Run it

Run the main notebook aurora-dataset.ipynb.

Data

The AURORA dataset is available from here.

A small sample is available here.