Underwater Geolocalization: polarization patterns enable new technology
University of Illinois Urbana-Champaign researchers have developed a novel method for underwater geolocalization
They used deep neural networks that have been trained on 10 million polarization-sensitive images collected from locations around the world.
This new study, led by electrical and computer engineering professor Viktor Gruev, along with computer science professor David Forsyth, enables underwater geolocalization using only optical data while providing a tool for tethered-free underwater navigation.
These findings were recently published in the journal eLight.
“We are showing for the first time, you can geolocate yourself, or a camera, in a number of different conditions, whether in open ocean waters, clear waters or low visibility waters, at day, at night, or at depth,” says Gruev. “Once you have a sense of where you are, then you can start exploring and use that information to have a better understanding of the underwater world or even how animals navigate.”
Gruev explains that one of the main challenges with underwater navigation and geolocalization is that GPS signals cannot penetrate water—they bounce off the surface. “We are blind in terms of GPS signals underwater. We need to use different means and different technology for geolocating underwater.”
The current standard for geolocalization is using acoustic information, mainly obtained by sonar technology. This works by deploying many little sonar beacons which send signals that are triangulated to locate an object underwater. The issue, however, is that sonar only works in a small, defined area, while also being limited by its accuracy.
Another method currently utilized is using submersibles that are tethered to a larger vessel above the surface that has a GPS signal. Although the submersible can maneuver a little bit, it is ultimately limited by the movement of the vessel.
“It is an incredibly challenging problem to have a free-moving, underwater vehicle. The way we solve this problem is by developing specialized cameras and machine learning algorithms. By combining those, we can actually figure out the sun’s location and this is where polarization imaging comes into play,” says Gruev.
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