Collision Detection and Avoidance for Underwater Vehicles Using Omnidirectional Vision
dc.contributor.author
dc.date.accessioned
2022-07-26T09:46:32Z
dc.date.available
2022-07-26T09:46:32Z
dc.date.issued
2022-07-18
dc.identifier.issn
1424-8220
dc.identifier.uri
dc.description.abstract
Exploration of marine habitats is one of the key pillars of underwater science, which often involves collecting images at close range. As acquiring imagery close to the seabed involves multiple hazards, the safety of underwater vehicles, such as remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs), is often compromised. Common applications for obstacle avoidance in underwater environments are often conducted with acoustic sensors, which cannot be used reliably at very short distances, thus requiring a high level of attention from the operator to avoid damaging the robot. Therefore, developing capabilities such as advanced assisted mapping, spatial awareness and safety, and user immersion in confined environments is an important research area for human-operated underwater robotics. In this paper, we present a novel approach that provides an ROV with capabilities for navigation in complex environments. By leveraging the ability of omnidirectional multi-camera systems to provide a comprehensive view of the environment, we create a 360° real-time point cloud of nearby objects or structures within a visual SLAM framework. We also develop a strategy to assess the risk of obstacles in the vicinity. We show that the system can use the risk information to generate warnings that the robot can use to perform evasive maneuvers when approaching dangerous obstacles in real-world scenarios. This system is a first step towards a comprehensive pilot assistance system that will enable inexperienced pilots to operate vehicles in complex and cluttered environments
dc.description.sponsorship
This research work was supported in part by the Secretaria d’Universitats i Recerca del
Departament d’economia i Coneixement de la Generalitat de catalunya under Grant 2021FI_B1_00154
for E. Ochoa; in part by Spanish project CTM2017-83075-R; by the European project EuroFleetsPlus
under grant H2020-INFRAIA-2018-2020-824077; and in part by Project PID2020-116736RV-IOO
(MINECO/FEDER, UE)
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
MDPI (Multidisciplinary Digital Publishing Institute)
dc.relation
CTM2017-83075-R
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Reproducció digital del document publicat a: https://doi.org/10.3390/s22145354
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Sensors, 2022, vol. 22, núm. 14, p. 5354
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Articles publicats (D-ATC)
dc.rights
Attribution 4.0 International
dc.rights.uri
dc.subject
dc.title
Collision Detection and Avoidance for Underwater Vehicles Using Omnidirectional Vision
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.relation.projectID
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/CTM2017-83075-R/ES/ROBOT SUBMARINO INTELIGENTE PARA LA EXPLORACION OMNIDIRECCIONAL E INMERSIVA DEL BENTOS/
info:eu-repo/grantAgreement/EC/H2020/824077/EU/An alliance of European marine research infrastructure to meet the evolving needs of the research and industrial communities./EurofleetsPlus
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.doi
dc.contributor.funder
dc.type.peerreviewed
peer-reviewed
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dc.relation.ProjectAcronym