Presentation + Paper
7 June 2024 SkyPole: a geolocation algorithm based on polarized vision without using astronomical ephemerides
Thomas Kronland-Martinet, Léo Poughon, Marcel Pasquinelli, David Duché, Julien R. Serres, Stéphane Viollet
Author Affiliations +
Abstract
Global Navigation Satellite Systems (GNSS) are widely used due to their easy access to outdoor GNSS signals and their spatial precision. However, such systems are sensitive to jamming and spoofing. Simple and robust navigation strategies can be found in animals deprived by essence of any GNSS system. Studies have shown that animals like bees or ants utilize the sky’s polarization pattern for navigation. We recently proposed a method inspired by migratory birds, which calibrate their magnetic compass through the celestial rotation of night stars or the daytime polarization pattern. By considering the temporal properties of the sky’s polarization pattern as a relevant navigation information, we developed a bio-inspired method to find the geographical north bearing and the observer’s latitude, requiring only skylight polarization observations during day. To reduce the noise susceptibility of our method, we added a pre-processing step using a least square method based on skylight polarization models, and a segmentation process based on a convolutional autoencoder neural network, trained with simulated data.
Conference Presentation
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Thomas Kronland-Martinet, Léo Poughon, Marcel Pasquinelli, David Duché, Julien R. Serres, and Stéphane Viollet "SkyPole: a geolocation algorithm based on polarized vision without using astronomical ephemerides", Proc. SPIE 13050, Polarization: Measurement, Analysis, and Remote Sensing XVI, 1305007 (7 June 2024); https://doi.org/10.1117/12.3013560
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KEYWORDS
Polarization

Sun

Cameras

Image segmentation

Data modeling

Neural networks

Rayleigh scattering

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