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Skylight Polarization Pattern Simulator Based on a Virtual-Real-Fusion Framework for Urban Bionic Polarization

Qianhui Li¹, Liquan Dong¹, Yao Hu¹

¹Key Laboratory for Precision Optoelectronic Measurement Instrument and Technology, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.

Sensors (Basel, Switzerland)

|August 12, 2023

Summary

This summary is machine-generated.

Acquiring diverse skylight polarization data for bionic navigation in cities is hard. This study introduces a virtual-real-fusion simulator to generate this data, aiding navigation research.

Keywords:

atmospheric model bionic polarization navigation data preparation machine learning polarimeter skylight polarization pattern

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Area of Science:

Robotics and Navigation
Computer Vision
Optics

Background:

Bionic polarization navigation relies on extensive skylight polarization data.
Urban environments pose challenges for acquiring diverse, ground-truthed polarization data.
Existing methods of pure simulation or measurement are insufficient.

Purpose of the Study:

To propose a novel virtual-real-fusion framework for simulating skylight polarization patterns.
To develop a data preparation method that integrates simulation and real-world measurements.
To generate diverse, scene-specific skylight polarization data for urban environments.

Main Methods:

A virtual component simulates skylight polarization patterns and ground truth.
A real component captures essential scene information through practical measurements.
A fusion component integrates virtual and real data based on imaging projection.
A simulator instance was developed and validated across 174 urban scenes.

Main Results:

The framework successfully generates large volumes of diverse urban skylight polarization pattern data.
The data includes scene information and complete ground truth.
The approach requires only a few practical measurements for data generation.
A dataset and open-source code were released to the research community.

Conclusions:

The virtual-real-fusion simulator effectively addresses the data acquisition challenges in urban bionic navigation.
The proposed method provides a valuable resource for researchers developing and testing polarization navigation systems.
The released dataset and code facilitate further advancements in the field.