Combining Remote and In-situ Sensing for Autonomous Underwater Vehicle Localization and Navigation
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Author/Creator ORCID
Date
2022-10-17
Type of Work
Department
Program
Citation of Original Publication
C. 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.
Rights
This 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.
Public Domain Mark 1.0
Public Domain Mark 1.0
Subjects
Abstract
Scientists 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.