AURORA, a multi-sensor dataset for robotic ocean exploration. (April 2022)

• Record Type: Journal Article
• Authors: Bernardi, Marco; Hosking, Brett; Petrioli, Chiara; Bett, Brian J; Jones, Daniel; Huvenne, Veerle AI; Marlow, Rachel; Furlong, Maaten; McPhail, Steve; Munafò, Andrea
• Is Part Of: International journal of robotics research. Volume 41:Number 5(2022)
• Page Start: 461
• Page End: 469
• Publication Date: 2022-04
• Subjects: Autonomous underwater vehicles -- imaging camera -- sidescan sonar -- multibeam echosounder -- underwater navigation; Robots -- Periodicals; Robots, Industrial -- Periodicals; 629.89205
• DOI: 10.1177/02783649221078612
• Languages: English
• ISSNs: 0278-3649

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, this paper presents a freely available dataset suitable to test control, navigation, sensor processing algorithms and others tasks. This dataset combines AUV navigation data, sidescan sonar, multibeam echosounder data and seafloor camera image data, and associated sensor acquisition metadata to provide a detailed characterisation of surveys carried out by the National Oceanography Centre (NOC) in the Greater Haig Fras Marine Conservation Zone (MCZ) of the U.K in 2015.