News Release

Navigating underwater inspired by migratory animals

Peer-Reviewed Publication

Light Publishing Center, Changchun Institute of Optics, Fine Mechanics And Physics, CAS

Earth's water surface is a complex and dynamic environment, encompassing vast oceans, seas, lakes, and rivers. The oceans alone account for over 70 per cent of theEarth's surface area and contain an estimated 97 per cent of the planet's water supply. In situ monitoring of water properties remains challenging despite its importance, and less precise satellite imaging is often used to capture water surface temperature, salinity, oxygen/nitrogen levels, and other parameters.

In a new paper published in eLight, a team of scientists led by Professor Viktor Gruev from the University of Illinois at Urbana-Champaign developed a novel underwater geolocation technique.

Autonomous underwater sampling robots can provide more accurate in situ monitoring, but reliable geolocalization is required for their successful operation. As satellite-based global positioning system (GPS) does not work in the underwater environment, alternative methods for underwater localization have been explored with limited success. Despite advancements in acoustic navigation, landmark identification, and inertial navigation, underwater geolocalization still has limited area coverage or poor global accuracy.

Small underwater vehicles and scuba divers face constraints on size and power for navigation devices, making precise inertial and long-base-line acoustic navigation impractical. Visual-based underwater geolocalization using color and polarization images has demonstrated limited accuracy and is only effective in clear waters and during the day. Therefore, submersible vehicles and scuba divers frequently lack reliable geolocalization, crucial for exploratory underwater missions.

Migratory animals provide examples of precise navigation and geolocalization in air and water, spanning the globe. These animals rely on various sensory cues, including polarization-sensitive information from the sky or water. Structured light polarization patterns are ubiquitous in both above- and underwater environments. The scattering of sunlight or moonlight in the upper atmosphere generates distinctive polarization patterns in the sky. Although humans cannot directly perceive light polarization, we may have utilized sky polarization patterns for navigation with appropriate viewing equipment.

In open ocean waters or oligotrophic fresh waters with a low scattering coefficient (0.001 m-1), a single scattering model can accurately represent underwater polarisation patterns. Therefore, straightforward inference procedures can be applied to achieve geolocalization in shallow clear water. However, in coastal ocean waters and eutrophic lakes where the scattering coefficient can be as high as 1 m-1, the single scattering model is inadequate for predicting underwater polarization information, as evidenced by the underwater polarization patterns captured with an omnidirectional lens.

The accuracy of predicted underwater polarization patterns can improve by utilizing multi-scattering models that rely on three-dimensional Monte Carlo techniques. However, integrating these models with geolocalization would necessitate the generation of underwater patterns at numerous locations worldwide, rendering the computational feasibility of such an approach unattainable. Similarly, underwater polarization patterns at night are influenced by both the moon and night sky contributions, making them challenging to model using the single scattering model, even in clear water at night or at greater depths. This underscores the importance of developing new methods for geolocalization that can handle high-scattering waters and low-light conditions.

The research team showed that even though direct inference through predictive models is unmanageable in many underwater situations, polarization patterns produced by daylight in low-visibility water and by nightlight in both high and low-visibility water allow accurate geolocalization.

The team collected ∼10 million images with underwater cameras capable of recording the radial polarization light field from four sites around the globe. They then trained a deep neural network to predict geolocation from underwater angle of polarization (AoP) images collected with an omnidirectional lens in combination with camera position sensor data.

The researchers systematically compared underwater geolocalization accuracy between parametric and data driven models across time, date and different water visibility. They demonstrated that using polarization information instead of intensity-only images results in superior geolocalization accuracy. For the first time reported in the literature, geolocalization at night, in low visibility waters, and at a depth of 50 meters in clear waters using transfer learning techniques.


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