# A stand-alone polarimetric acquisition system for producing a long-term skylight dataset

Léo POUGHON,¹,² Jocelyn MONNOYER¹,², Stéphane VIOLLET¹, Vincent AUBRY², and Julien R SERRES¹,³

¹Aix Marseille Univ., CNRS, ISM, Marseille, France

²Stellantis, Velizy-Villacoublay, France

³Institut Universitaire de France, Paris, France

## Context

In nature, some desert ants [1] are able to navigate without GPS using a heading derived from sun position by measuring skylight polarization.

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How does varying weather change the skylight polarization pattern?

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**Goal:** create a long-term database of the skylight polarization in a fixed location

## Methods

State-of-the art for skylight polarization imaging use a Division of Focal Plane (DoFP) sensor with a fisheye lens. [2]

→ We use a color-polarization camera (Sony IMX250MYR sensor) fixated on a rotative telescope mount with a fisheye lens (Fujinon FE185C057HA-1)

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Camera left on a distant roof → Wide view angle and vandalism-free, with weather protection and autonomous solar power source.

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Mechanical layout

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The hardware architecture of the setup.

## Orientation calibration

→ Camera frame (9CAM) orientation measured in respect to the local East-North-Up (9ENU) frame. To do this, we chose to compare the sun course measured on the sensor with sun ephemeris provided by the Astropy Python package [4]

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## Results

→ Several thousands of images were taken between 2022-07-29 and 2022-08-25.

→ One image acquired every 10 minutes with several fixed and auto-adaptive exposure times. Camera orientation was assumed to be constant over time.

→ Comparison with Rayleigh model of sky simulated for similar time, location and optical sensor.

Image: 2022-08-02T07:20:36 - Color channel: B - Expo: 1000.032 µs

Sun Position - La Timne (Marseille, France) - 2022-08-02

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Rayleigh Model AOP

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Rayleigh Model DOP

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RGB Image

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Measured AOP

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Measured DOP

Dataset taken from a laboratory rooftop in Marseille, France (43.286990365824785°N, 5.403361407820939°E).

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Database is available here: https://doi.org/10.5774/6300

→ Stand-alone system based on a polarimetric camera for building a database of images.

→ Open-source tool in Python to visualize and compare the real images with simulated images obtained with the Rayleigh model.

→ Our database could be useful for testing and developing neural network-based and classical image processing, or to validate models of the polarized skylight patterns in various outdoor conditions.

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<footnote>[1] G. Horváth et D. Varjú, Polarized Light in Animal Vision. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. doi: 10.1007/978-3-662-09387-0</footnote>

<footnote>[2] F. Kong, Y. Guo, J. Zhang, X. Fan, and X. Guo, Review on bio-inspired polarized skylight navigation. Chinese Journal of Aeronautics, 2023</footnote>

<footnote>[3] Newville, Matthew et al., LMFIT: Non-Linear Least-Square Minimization and Curve-Fitting for Python, 2014</footnote>

<footnote>This work made use of Astropy (http://www.astropy.org), a community-developed core Python package and an ecosystem of tools and resources for astronomy</footnote>

<footnote>* This research was supported by the Openlab "Automotive MotionLab" part of the SteLLab network.</footnote>