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Collision Detection and Avoidance for Underwater Vehicles Using Omnidirectional Vision †

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 under...

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Autores principales: Ochoa, Eduardo, Gracias, Nuno, Istenič, Klemen, Bosch, Josep, Cieślak, Patryk, García, Rafael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9315794/
https://www.ncbi.nlm.nih.gov/pubmed/35891038
http://dx.doi.org/10.3390/s22145354
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author Ochoa, Eduardo
Gracias, Nuno
Istenič, Klemen
Bosch, Josep
Cieślak, Patryk
García, Rafael
author_facet Ochoa, Eduardo
Gracias, Nuno
Istenič, Klemen
Bosch, Josep
Cieślak, Patryk
García, Rafael
author_sort Ochoa, Eduardo
collection PubMed
description 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.
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spelling pubmed-93157942022-07-27 Collision Detection and Avoidance for Underwater Vehicles Using Omnidirectional Vision † Ochoa, Eduardo Gracias, Nuno Istenič, Klemen Bosch, Josep Cieślak, Patryk García, Rafael Sensors (Basel) Article 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. MDPI 2022-07-18 /pmc/articles/PMC9315794/ /pubmed/35891038 http://dx.doi.org/10.3390/s22145354 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ochoa, Eduardo
Gracias, Nuno
Istenič, Klemen
Bosch, Josep
Cieślak, Patryk
García, Rafael
Collision Detection and Avoidance for Underwater Vehicles Using Omnidirectional Vision †
title Collision Detection and Avoidance for Underwater Vehicles Using Omnidirectional Vision †
title_full Collision Detection and Avoidance for Underwater Vehicles Using Omnidirectional Vision †
title_fullStr Collision Detection and Avoidance for Underwater Vehicles Using Omnidirectional Vision †
title_full_unstemmed Collision Detection and Avoidance for Underwater Vehicles Using Omnidirectional Vision †
title_short Collision Detection and Avoidance for Underwater Vehicles Using Omnidirectional Vision †
title_sort collision detection and avoidance for underwater vehicles using omnidirectional vision †
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9315794/
https://www.ncbi.nlm.nih.gov/pubmed/35891038
http://dx.doi.org/10.3390/s22145354
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