Vehicle Roll Angle Estimation via Brightness Gradient Skewness
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Solution Overview
Problem
Conventional roll angle estimation methods face challenges in achieving accurate estimation, particularly when the number of detected straight lines is insufficient, and in verifying the inclination angle of captured images without sensors, leading to potential errors in determining vertical or horizontal components.
Innovation Solution
A vehicle roll angle estimation system that utilizes a brightness gradient orientation histogram to calculate skewness, allowing for accurate roll angle estimation by analyzing the frequency distribution of brightness gradient orientations in images captured from a vehicle, with the option to incorporate an internal sensor for verification and complementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional straight line detection methods are used for roll angle estimation, then the system can operate without additional sensors, but the estimation accuracy deteriorates when the number of detected straight lines is insufficient
Solution Approach 1:
The invention transforms the approach from detecting straight lines to analyzing brightness gradient orientations. By calculating the orientation of brightness gradients in pixel blocks and generating a histogram of these orientations, the system can determine roll angle even when traditional straight line detection fails due to insufficient line counts.
Solution Approach 2:
The invention replaces the mechanical/geometric approach of straight line detection with an optical field-based approach using brightness gradient analysis. Instead of relying on geometric features (straight lines), the system uses photometric properties (brightness gradients) to achieve more robust roll angle estimation.
2Device complexity
If image processing alone is used without sensors, then the system structure is simplified, but it becomes impossible to verify the inclination angle of captured images
Solution Approach 1:
The invention enables the image processing system to self-verify its results by analyzing the symmetry of brightness gradient orientation histograms. The system uses the inherent symmetrical properties of the histogram to determine whether the detected inclination angle is correct, eliminating the need for external sensors while maintaining verification capability.
Solution Approach 2:
The invention introduces a feedback mechanism where the symmetry analysis of the brightness gradient orientation histogram provides verification information. The system checks whether the histogram exhibits expected symmetrical patterns to confirm the accuracy of the detected roll angle, creating a self-correcting system.
3Measurement precision
If brightness gradient orientation histogram analysis is used, then roll angle estimation accuracy is improved, but the computational complexity increases
Solution Approach 1:
The invention divides the image into multiple pixel blocks and processes each block independently to calculate brightness gradient orientations. This segmentation approach allows parallel processing and reduces the computational burden compared to analyzing the entire image as a single unit, while still achieving accurate roll angle estimation through histogram aggregation.
Data Source
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AI summary
A vehicle roll angle estimation system (10) includes: an imaging device (40) capable of capturing forward or rearward images from the vehicle; and a roll angle estimation device (20) for obtaining captured images taken by the imaging device (40) and analyzing the images thereby estimating a roll angle of the imaging device (40) which changes as the vehicle is inclined. The roll angle estimation device (20) includes: a histogram generator (22) which generates a brightness gradient orientation histogram indicating frequency distribution of brightness gradient orientation of a plurality of pixels contained in the image; and an estimation section (23) which calculates a skewness indicating a symmetricalness of the brightness gradient histogram, and estimates a roll angle based on the calculated skewness.