Polarization Orientation With Sky Priors and Morphological Matching
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Solution Overview
Problem
Current navigation methods using the angle of polarization (AOP) are sensitive to local polarization interferences from objects like trees and buildings, leading to increased calculation errors and requiring additional sensors to detect sun blur, thereby increasing navigation costs.
Innovation Solution
A polarization orientation method utilizing sky region priors and morphological template matching in the transform domain, which includes calculating polarization dark channels, gradients, and binary images to identify a sky region, projecting onto a two-dimensional plane, and using template matching to solve sun blur without additional sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If direct AOP measurement is used for navigation, then navigation information can be obtained, but sun blur detection requires additional sensors increasing navigation cost
Solution Approach 1:
The polarization camera performs multiple functions: it captures both the angle of polarization (AOP) for navigation and the degree of polarization (DoP) for sun blur detection simultaneously. The sky region identification module uses DoP gradients to detect sun blur without requiring separate sensors, making the system multi-functional and cost-effective
Solution Approach 2:
The system uses its own polarization imaging capability to detect sun blur through sky region identification. The sun blur detection is performed internally by analyzing DoP gradients in the captured images, eliminating the need for external additional sensors and making the system self-sufficient
2Measurement precision
If traditional polarization orientation method is used, then course angle measurement can be completed, but reflected light from objects destroys AOP model reducing accuracy
Solution Approach 1:
The method segments the image into sky regions and non-sky regions using polarization dark channel clustering and sky region identification. By isolating sky regions where Rayleigh scattering dominates, the system excludes areas affected by reflected light from objects, thereby maintaining measurement precision
Solution Approach 2:
The system applies different processing strategies to different regions: sky regions use AOP for orientation while non-sky regions are identified and excluded or handled separately using DoP gradient analysis. This local differentiation ensures that reflected light from objects does not contaminate the orientation measurement
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method achieves accurate and robust course angle measurement under interference from objects, reducing errors and eliminating the need for additional sensors, thus enhancing navigation accuracy and robustness.
Implementation Method 1
an angle of polarization (AOP) predicted by a single Rayleigh scattering model has robustness and contains a lot of navigation information
Data Source
AI summary
A polarization orientation method based on sky region prior and morphological template matching of transform domain, which reduces an interference of reflected light through the sky region prior and solves sun blur through the morphological template matching of transform domain (MTMTD); a region division standard is given through prior knowledge of a sky region, and the sky region is identified by clustering darkest pixel images in 15-channel polarization images; meanwhile, the MTMTD strategy provided carries out a transform domain treatment on an image of the angle of polarization by an imaging method, and solves the problem of sun blur under a single Rayleigh scattering model without relying on an additional sensor.