AUV Abyss workflow: autonomous deep sea exploration for ocean research

Klischies, Meike; Rothenbeck, Marcel; Steinfuhrer, Anja; Yeo, Isobel A. ORCID: https://orcid.org/0000-0001-9306-3446; Ferreira, Christian dos Santos; Mohrmann, Jochen; Faber, Claas; Schirnick, Carsten. 2018 AUV Abyss workflow: autonomous deep sea exploration for ocean research. In: 2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV), Porto, Portugal, 6-9 Nov 2018. IEEE, 1-6.

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Official URL: http://dx.doi.org/10.1109/AUV.2018.8729722

Abstract/Summary

Autonomous underwater vehicles (AUVs) equipped with multibeam echosounders (MBES) are essential for collecting high-resolution bathymetric data in the deep sea. Navigation of AUVs and accuracy of acquired MBES data is challenging, especially in deep water or rough terrain. Here, we present the AUV Abyss operational workflow that uses mission planning together with a long baseline (LBL) positioning network, and systematic post-processing of the MBES data using feature matching. The workflow enables autonomous exploration even in difficult terrain, makes ultrashort baseline navigation during the AUV survey obsolete and with this, increases the efficiency of ship time. It provides an efficient workflow for multi-survey mapping campaigns to produce high-resolution, large-coverage seafloor maps. Automated documentation of post-processing steps enhances the archiving of produced results, facilitates knowledge transfer, adaptation to other systems and management of large datasets. Comprehensive documentation allows developing routines that provide a first step towards automatization of AUV operations and MBES data processing.

Item Type:	Publication - Conference Item (Paper)
Digital Object Identifier (DOI):	https://doi.org/10.1109/AUV.2018.8729722
Date made live:	17 Jun 2019 11:38 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/523763

Item Type:

Publication - Conference Item (Paper)

Digital Object Identifier (DOI):

https://doi.org/10.1109/AUV.2018.8729722

Date made live:

17 Jun 2019 11:38 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/523763