Polarization-based underwater geolocalization with deep learning

Abstract

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 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 absence of identifiable landmarks. In this paper, we present a novel method for underwater geolocalization using deep neural networks trained on sim10 million polarization-sensitive images acquired globally, along with camera position sensor data. Our approach achieves longitudinal accuracy of sim55 km (sim1000 km) during daytime (nighttime) at depths up to sim8 m, regardless of water turbidity. In clear waters, the transfer learning longitudinal accuracy is sim255 km at 50 m depth. By leveraging optical data in conjunction with camera position information, our novel method facilitates underwater geolocalization and offers a valuable tool for untethered underwater navigation.