Remote awareness of image quality for multiweek shore-launched AUV surveys

Abstract

Visual seafloor imaging using autonomous underwater vehicles (AUVs) has become an established method for seafloor mapping and monitoring. With AUVs now achieving multiweek endurance and several hundred kilometers of range on a single charge, image quality assessment (IQA) on-board vehicles in the field is necessary for robust data acquisition given the sensitivity of underwater imaging surveys to environmental conditions. This research develops a metric to assess seafloor image quality in situ, and demonstrates its use for quality assurance during a 21-day, shore-launched AUV campaign that visited three sites up to 170 km from shore. The metric was transmitted via satellite communication along with vehicle telemetry to shore-based AUV operators during regular surfacing intervals without relying on physical vehicle recovery. The method was implemented on the seafloor laser scan and strobed imaging system BioCam, deployed on the Autosub Long Range (ALR) AUV (also known as Boaty McBoatface) in the North Sea. Several tens of hectares of seafloor imagery were collected, and image quality scores were transmitted. This information was used to retask the AUV and maximize the quality of acquired images within operational constraints. Data products generated from the collected imagery show the improvements achieved that would otherwise have been missed. This highlights the importance of remote awareness of data quality to facilitate longer and consecutive mapping missions without reliance on physical vehicle recovery.

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Identifiers

ORCID for Adrian Bodenmann: orcid.org/0000-0002-3195-0602

ORCID for Alexander B. Phillips: orcid.org/0000-0003-3234-8506

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