Title: "Bio-Inspired Sensors for Underwater Geolocalization.”
Water is an essential component of the Earth’s climate, but monitoring its properties using autonomous underwater sampling robots remains a significant challenge due to the lack of underwater geolocalization capabilities. Current methods for underwater geolocalization rely on tethered systems with limited coverage or daytime imagery data in clear waters, leaving much of the underwater environment unexplored. Geolocalization in turbid waters or at night has been considered unfeasible due to the absence of identifiable landmarks. In this talk, Dr. Gruev will discuss a novel method for underwater geolocalization using bioinspired polarization sensors and deep neural networks trained on ∼10 million polarization-sensitive images acquired globally. His team’s approach achieves longitudinal accuracy of ∼55 km during daytime at depths up to ∼8 m, regardless of water turbidity. In clear waters, the transfer learning longitudinal accuracy is ∼255 km at 50 m depth. By leveraging optical data, the team’s novel method facilitates underwater geolocalization and offers a valuable tool for untethered underwater navigation.