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

Author/Creator ORCID





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.


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.
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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.