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Real-Time Underwater StereoFusion

Many current and future applications of underwater robotics require real-time sensing and interpretation of the environment. As the vast majority of robots are equipped with cameras, computer vision is playing an increasingly important role it this field. This paper presents the implementation and e...

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Detalles Bibliográficos
Autores principales: Rossi, Matija, Trslić, Petar, Sivčev, Satja, Riordan, James, Toal, Daniel, Dooly, Gerard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6263622/
https://www.ncbi.nlm.nih.gov/pubmed/30441826
http://dx.doi.org/10.3390/s18113936
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author Rossi, Matija
Trslić, Petar
Sivčev, Satja
Riordan, James
Toal, Daniel
Dooly, Gerard
author_facet Rossi, Matija
Trslić, Petar
Sivčev, Satja
Riordan, James
Toal, Daniel
Dooly, Gerard
author_sort Rossi, Matija
collection PubMed
description Many current and future applications of underwater robotics require real-time sensing and interpretation of the environment. As the vast majority of robots are equipped with cameras, computer vision is playing an increasingly important role it this field. This paper presents the implementation and experimental results of underwater StereoFusion, an algorithm for real-time 3D dense reconstruction and camera tracking. Unlike KinectFusion on which it is based, StereoFusion relies on a stereo camera as its main sensor. The algorithm uses the depth map obtained from the stereo camera to incrementally build a volumetric 3D model of the environment, while simultaneously using the model for camera tracking. It has been successfully tested both in a lake and in the ocean, using two different state-of-the-art underwater Remotely Operated Vehicles (ROVs). Ongoing work focuses on applying the same algorithm to acoustic sensors, and on the implementation of a vision based monocular system with the same capabilities.
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spelling pubmed-62636222018-12-12 Real-Time Underwater StereoFusion Rossi, Matija Trslić, Petar Sivčev, Satja Riordan, James Toal, Daniel Dooly, Gerard Sensors (Basel) Article Many current and future applications of underwater robotics require real-time sensing and interpretation of the environment. As the vast majority of robots are equipped with cameras, computer vision is playing an increasingly important role it this field. This paper presents the implementation and experimental results of underwater StereoFusion, an algorithm for real-time 3D dense reconstruction and camera tracking. Unlike KinectFusion on which it is based, StereoFusion relies on a stereo camera as its main sensor. The algorithm uses the depth map obtained from the stereo camera to incrementally build a volumetric 3D model of the environment, while simultaneously using the model for camera tracking. It has been successfully tested both in a lake and in the ocean, using two different state-of-the-art underwater Remotely Operated Vehicles (ROVs). Ongoing work focuses on applying the same algorithm to acoustic sensors, and on the implementation of a vision based monocular system with the same capabilities. MDPI 2018-11-14 /pmc/articles/PMC6263622/ /pubmed/30441826 http://dx.doi.org/10.3390/s18113936 Text en © 2018 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Rossi, Matija
Trslić, Petar
Sivčev, Satja
Riordan, James
Toal, Daniel
Dooly, Gerard
Real-Time Underwater StereoFusion
title Real-Time Underwater StereoFusion
title_full Real-Time Underwater StereoFusion
title_fullStr Real-Time Underwater StereoFusion
title_full_unstemmed Real-Time Underwater StereoFusion
title_short Real-Time Underwater StereoFusion
title_sort real-time underwater stereofusion
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6263622/
https://www.ncbi.nlm.nih.gov/pubmed/30441826
http://dx.doi.org/10.3390/s18113936
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