Combining Remote and In-situ Sensing for Autonomous Underwater Vehicle Localization and Navigation

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dc.contributor.authorRojas, Cesar A.
dc.contributor.authorPadrao, Paulo V.
dc.contributor.authorFuentes, Jose E.
dc.contributor.authorAlbayrak, Arif R.
dc.contributor.authorOsmanoglu, Batuhan
dc.contributor.authorBobadilla, Leonardo
dc.date.accessioned2023-01-09T15:27:03Z
dc.date.available2023-01-09T15:27:03Z
dc.date.issued2022-10-17
dc.descriptionOCEANS 2022, Hampton Roads, 17-20 Oct. 2022en_US
dc.description.abstractScientists continue to study the red tide and fish-kill events happening in Florida. Machine learning applications using remote sensing data on coastal waters to monitor water quality parameters and detect harmful algal blooms are also being studied. Unmanned Surface Vehicles (USVs) and Autonomous Underwater Vehicles (AUVs) are often deployed on data collection and disaster response missions. To enhance study and mitigation efforts, robots must be able to use available data to navigate these underwater environments. In this study, we compute a satellite-derived underwater environment (SDUE) model by implementing a supervised machine learning model where remote sensing reflectance (Rrs) indices are labeled with in-situ data they correlate with. The models predict bathymetry and water quality parameters given a recent remote sensing image. In our experiment, we use Sentinel-2 (S2) images and in-situ data of the Biscayne Bay to create an SDUE that can be used as a Chlorophyll-a map. The SDUE is then used in an Extended Kalman Filter (EKF) application that solves an underwater vehicle localization and navigation problem.en_US
dc.description.sponsorshipNOWLEDGMENT This work is supported in part by the NSF grants IIS2034123, IIS-2024733, the U.S. Dept. of Homeland Security grant 2017-ST-062000002, the National GEM Consortium, the Office of Naval Research, and the ESA Network of Resources Initiative. We also acknowledge the equipment loan from the Florida International University Institute of Environmenten_US
dc.description.urihttps://ieeexplore.ieee.org/abstract/document/9977208/en_US
dc.format.extent7 pagesen_US
dc.genreconference papers and proceedingsen_US
dc.identifierdoi:10.13016/m2wt4g-9ha7
dc.identifier.citationC. A. Rojas, P. V. Padrao, J. E. Fuentes, A. R. Albayrak, B. Osmanoglu and L. Bobadilla, "Combining Remote and In-situ Sensing for Autonomous Underwater Vehicle Localization and Navigation," OCEANS 2022, Hampton Roads, 2022, pp. 1-7, doi: 10.1109/OCEANS47191.2022.9977208.en_US
dc.identifier.urihttps://doi.org/10.1109/OCEANS47191.2022.9977208
dc.identifier.urihttp://hdl.handle.net/11603/26598
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Joint Center for Earth Systems Technology
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.en_US
dc.rightsPublic Domain Mark 1.0*
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/*
dc.titleCombining Remote and In-situ Sensing for Autonomous Underwater Vehicle Localization and Navigationen_US
dc.typeTexten_US

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