AURORA, A multi sensor dataset for robotic ocean exploration

Citation Author(s):
Marco
Bernardi
University of Rome, La Sapienza
Brett
Hosking
National Oceanography Centre
Chiara
Petrioli
University of Rome, La Sapienza
Brian J.
Bett
National Oceanography Centre
Daniel
Jones
National Oceanography Centre
Veerle A.I.
Huvenne
National Oceanography Centre
Rachel
Marlow
National Oceanography Centre
Maaten
Furlong
National Oceanography Centre
Steve
McPhail
National Oceanography Centre
Andrea
Munafo
National Oceanography Centre
Submitted by:
Andrea Munafo
Last updated:
Tue, 11/09/2021 - 03:16
DOI:
10.21227/nnms-te61
License:
Creative Commons Attribution
3764 Views
Categories:
Artificial Intelligence
Machine Learning
Other
Image Processing
Computer Vision
Climate Change/Environmental
Geoscience and Remote Sensing
Sensors
Remote Sensing
Keywords:
autonomous vehicles; Field robotics; AUVs; side scan sonars; multibeam sonars; doppler velocity loggers; sub bottom profilers; Navigation; localisation; Control systems; Robotics; Reproducible Research;
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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.

Instructions: 

This repository contains the AURORA dataset, a multi sensor dataset for robotic ocean exploration.

It is accompanied by 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.

Exemplar python code is provided at https://github.com/noc-mars/aurora.

 

The dataset provided in this repository includes data collected during cruise James Cook 125 (JC125) of the National Oceanography Centre, using the Autonomous Underwater Vehicle Autosub 6000. It is composed of two AUV missions: M86 and M86.

  • M86 contains a sample of multi-beam echosounder data in .all format. It also contains CTD and navigation data in .csv format.

  • M87 contains a sample of the camera and side-scan sonar data. The camera data contains 8 of 45320 images of the original dataset. The camera data are provided in .raw format (pixels are ordered in Bayer format). The size of each image is of size 2448x2048. The side-scan sonar folder contains a one ping sample of side-scan data provided in .xtf format.

  • The AUV navigation file is provided as part of the data available in each mission in .csv form.

 

The dataset is approximately 200GB in size. A smaller sample is provided at https://github.com/noc-mars/aurora_dataset_sample and contains a sample of about 200MB.

Each individual group of data (CTD, multibeam, side scan sonar, vertical camera) for each mission (M86, M87) is also available to be downloaded as a separate file. 

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