Stereo Camera Calibration via Homography Matching
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Solution Overview
Problem
Existing methods for calibrating stereo camera arrangements are complex and require substantial calculation capacity, making them inefficient for large-scale applications and environments with changing physical conditions, such as vehicles, where cameras need frequent adjustments.
Innovation Solution
A method that involves receiving overlapping images from two cameras, detecting candidate points, matching them to calculate homographies, and adjusting intrinsic or extrinsic parameters of each camera based on these homographies, without the need for specific reference points, using algorithms like the Harris corner detection method.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration methods are used, then calibration accuracy can be maintained, but the complexity of the calibration process increases substantially
Solution Approach 1:
The patent extracts and removes the requirement for complex reference markers and specialized calibration objects from the calibration process. Instead, it uses naturally occurring features and points from the environment that are visible to both cameras, thereby simplifying the calibration setup while maintaining accuracy through robust feature matching algorithms
Solution Approach 2:
The calibration method enables the stereo camera system to calibrate itself using its own captured images and naturally visible features in the scene. The system automatically identifies corresponding points between left and right camera images and computes calibration parameters without requiring external intervention or specialized calibration equipment
2Measurement precision
If complex calibration methods with substantial calculation capacity are applied, then calibration precision is maintained, but the productivity and speed of calibration decreases
Solution Approach 1:
The calibration process is segmented into distinct stages: feature detection in individual images, matching corresponding features between stereo images, and computing calibration parameters. This segmentation allows for efficient processing at each stage and enables parallel computation where applicable, improving overall calibration speed while maintaining precision
Solution Approach 2:
The method uses a sufficient number of feature points to achieve accurate calibration without requiring exhaustive computation. By selecting key corresponding points that provide adequate geometric constraints, the system achieves calibration precision with reduced computational burden compared to methods that process all possible image data
3Adaptability or versatility
If manual adjustment of intrinsic parameters is performed, then adaptability to changing environmental conditions is achieved, but the time and labor required for calibration increases
Solution Approach 1:
The calibration method incorporates feedback mechanisms where the system continuously monitors image quality and feature matching results, automatically adjusting calibration parameters to optimize performance. This feedback loop enables the system to adapt to changing environmental conditions such as vibrations, temperature changes, or lens shifts without requiring manual intervention
Solution Approach 2:
The system performs self-calibration by automatically detecting features, computing correspondence between stereo images, and adjusting intrinsic parameters based on the calculated homography. This self-service capability eliminates the need for manual parameter adjustment while maintaining adaptability to environmental changes, significantly reducing calibration time and labor requirements
4Measurement precision
If additional reference markers or calibration objects are used, then calibration accuracy is improved, but the cost and device complexity increases
Solution Approach 1:
The patent removes the requirement for expensive specialized calibration markers and reference objects from the calibration process. Instead, it extracts and utilizes naturally occurring features from the environment that are already visible to the cameras, thereby eliminating additional hardware costs while maintaining calibration accuracy through robust feature detection and matching algorithms
Data Source
Figure 1a~1b
Figure 2a~2b
Figure 3
AI summary
Method and arrangement for calibrating stereo cameras in a vehicle. A first image from the first camera is received (300), and a second image is received (302) from the second camera, wherein both the first image and the second image comprise an overlap. Furthermore, one or more candidate points of the first image is determined (304) in the overlap, and one or more candidate points of the second image is determined (306) in the overlap. Moreover, the candidate points of the first image are matched (308) with the candidate points of the second image and pairs of corresponding candidate points are determined. A homography of the first camera is calculated (310) based on the pairs of corresponding candidate points and a homography of the second camera is calculated based on the pairs of corresponding candidate points. The first camera is calibrated (312) by adjusting an intrinsic parameter or an extrinsic parameter of the first camera, based on the calculated (310) homography of the first camera. The second camera is calibrated (312) by adjusting an intrinsic parameter or an extrinsic parameter of the second camera, based on the calculated (310) homography of the second camera.