Monitoring of Benthic Reference Sites: Using an Autonomous Underwater Vehicle

Williams, Stefan B.; Pizarro, Oscar; Jakuba, Michael V.; Johnson, Craig R.; Barrett, Neville S.; Babcock, Russell C.; Kendrick, Gary A.; Steinberg, Peter D.; Heyward, Andrew; Doherty, Peter; Mahon, Ian; Johnson-Roberson, Matthew; Steinberg, Daniel; Friedman, Ariell

doi:10.1109/mra.2011.2181772

Cited by 168 publications

(88 citation statements)

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“…However, advancing our understanding of how structural complexity influences reef dynamics still requires improving our efficiency and ability to quantify multiple metrics of 3D structural complexity in a repeatable way, across spatial extents and maintaining sub-meter resolution [27,28]. Emerging close-range photogrammetric techniques allow the quantification of surface rugosity, among other structural complexity metrics, across a range of spatial extents, and at millimetre to centimetre-level resolution [29][30][31][32][33]. Much of the work in the aforementioned articles builds on a substantial body of work in 3D reconstruction, Optical Flow, and Structure from Motion (SfM) in the terrestrial domain; see [34][35][36][37], among others.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Multiscale Habitat Structural Complexity: A Cost-Effective Framework for Underwater 3D Modelling

Ferrari

McKinnon²,

et al. 2016

Remote Sensing

103

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Coral reef habitat structural complexity influences key ecological processes, ecosystem biodiversity, and resilience. Measuring structural complexity underwater is not trivial and researchers have been searching for accurate and cost-effective methods that can be applied across spatial extents for over 50 years. This study integrated a set of existing multi-view, image-processing algorithms, to accurately compute metrics of structural complexity (e.g., ratio of surface to planar area) underwater solely from images. This framework resulted in accurate, high-speed 3D habitat reconstructions at scales ranging from small corals to reef-scapes (10s km 2 ). Structural complexity was accurately quantified from both contemporary and historical image datasets across three spatial scales: (i) branching coral colony (Acropora spp.); (ii) reef area (400 m 2 ); and (iii) reef transect (2 km). At small scales, our method delivered models with <1 mm error over 90% of the surface area, while the accuracy at transect scale was 85.3%˘6% (CI). Advantages are: no need for an a priori requirement for image size or resolution, no invasive techniques, cost-effectiveness, and utilization of existing imagery taken from off-the-shelf cameras (both monocular or stereo). This remote sensing method can be integrated to reef monitoring and improve our knowledge of key aspects of coral reef dynamics, from reef accretion to habitat provisioning and productivity, by measuring and up-scaling estimates of structural complexity.

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Section: Introductionmentioning

confidence: 99%

Quantifying Multiscale Habitat Structural Complexity: A Cost-Effective Framework for Underwater 3D Modelling

Ferrari

McKinnon²,

et al. 2016

Remote Sensing

103

View full text Add to dashboard Cite

show abstract

“…Besides this aspect, the design, development and adoption of new technologies can increase the outcomes of this type of cooperation. In fact, in the recent years there is an increasing interest from the robotic community in the realization of user-friendly technologies and tools to carry out efficiently activities in the underwater environment [10][11][12][13][14][15]. What the marine community is lacking is an integrated system where all the processes of acquisition, processing, analysis and output representation are gathered in a unique solution.…”

mentioning

confidence: 99%

DocuScooter: An Innovative Underwater Scooter add-on for Scuba Diving and Citizen Science

Panebianco

Scaradozzi

Ciuccoli

et al. 2017

IRATJ

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In order to obtain robust and measurable data from the marine environment, citizen science projects need user-friendly tools that, during dives, autonomously gather information in an easy, low-cost and integrated way. Data collected from different devices and by different untrained divers must have good estimation of the position where the information has been acquired. With this goal, in the context of the Green Bubbles project, a novel platform, called DocuScooter has been designed and developed. DocuScooter implements all the algorithms to filter and merge data with correct timestamp and position preparing the complete mission report. After the mission, the diver can upload the report on an appropriate web service to produce a 3D documentation. In order to obtain the underwater position during the dive, a tailored device compatible with the platform is also presented. Result of the prototype of the platform and the first results of the position estimation algorithm are presented and discussed.

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“…The images have been taken by an underwater camera mounted in an autonomous underwater vehicle [Williams et al, 2012] during different real underwater interventions in different locations. The images have been divided into 6 groups depending on the characteristics of the images and the location of the acquisition.…”

Section: Image Datasetsmentioning

confidence: 99%

“…These datasets were acquired by the AUV Sirius [Williams et al, 2012] of the Australian Centre for Field Robotics in the University of Sydney, Australia who also provided and prepared the data.…”

Section: Publicationsmentioning

confidence: 99%

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Visibility in underwater robotics: Benchmarking and single image dehazing

Soler¹

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A mis padres y hermanos. AcknowledgementsThis thesis is the result of more than 5 years of work and it would have been impossible without the support of family, friends and colleagues. May the reader forgive me the discourtesy of following my wish to acknowledge their support in my mother tongue.But before changing to my mother tongue, I want to thank professor Stefan Williams for hosting me at the Australian Centre for Field Robotics in Sydney for four months. Working with his group, and specially with Mitch Bryson and Dushyant Rao, was crucial for the development of the final chapters of this thesis. I also have to acknowledge the ACFR for providing and preparing the datasets used in the deep learning experiments.En primer lugar, me gustaría agradecer a mis directores Raúl Marín y Pedro Sanz la oportunidad de poder trabajar en un campo tan motivante y desafiante como la robótica submarina a través de distintos proyectos en la frontera del conocimiento como Trident, Triton o Merbots. Además de por la ayuda y los consejos desde la experiencia a la hora de introducirme en la investigación.Gracias a la Universitat Jaume I por haber financiado durante 4 años el desarrollo de esta tesis a través del plan de promoción de la investigación (referencia de la ayuda PREDOC/2012/47).También tengo que dar las gracias a todos los miembros del IRSLab de los que he podido aprender, y con los que he colaborado y trabajado como un verdadero equipo durante estos años. A todos quiero dar gracias, Juan Carlos por su disponibilidad a la hora de ayudar, Javi por su espíritu de equipo, Toni por su decisión a la hora de afrontar problemas, David por susánimos y voluntad, Jorge por su insistencia en publicar, José Bernabé por sus consejos y Diego por su entrega.Gracias también a Mario, pese al poco tiempo que coincidimos demostró ser un investigador sobresaliente y un compañero ejemplar que me ayudó en los primeros momentos de este trabajo. Además también tengo que agradecer al RobInLab, especialmente aÁngel, Carlos, Marco, Xavi, Gabriel, Majd y Gustavo, por su ayuda y discusiones en los descansos delante de una taza de café.Durante mi estancia en Sydney tuve la suerte de coincidir con un pequeño grupo de españoles que hizo la estancia mucho más amena. Por ello también quiero darles las gracias, en particular a Guillem y a Raúl con los cuales compartí mucho tiempo dentro y fuera del laboratorio visitando y conociendo Australia, y en general a todas las personas con las que coincidí durante esos meses.Gracias a mi familia y amigos de la Venta del Moro, siempre dispuestos a escuchar después de una larga semana de trabajo. Durante estos años han sido v una distracción necesaria a los problemas del día a día, haciendo el camino de esta tesis mucho más entretenido.Quiero también agradecer a Loli por ser una más de la familia y preocuparse y cuidar de mí día a día desde bien pequeño, ayudándome a convertirme en una persona mejor.Porúltimo, quiero agradecer especialmente la ayuda y el apoyo de mi familia. A mis padres Luis y MaríaÁngeles que m...

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Monitoring of Benthic Reference Sites: Using an Autonomous Underwater Vehicle

Cited by 168 publications

References 20 publications

Quantifying Multiscale Habitat Structural Complexity: A Cost-Effective Framework for Underwater 3D Modelling

Quantifying Multiscale Habitat Structural Complexity: A Cost-Effective Framework for Underwater 3D Modelling

DocuScooter: An Innovative Underwater Scooter add-on for Scuba Diving and Citizen Science

Visibility in underwater robotics: Benchmarking and single image dehazing

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