Stereo Camera Auto-Calibration via Structure-from-Motion
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Solution Overview
Problem
Calibrating stereo cameras installed behind a vehicle windshield for driver assistance systems is challenging due to windshield distortion and the need for time-consuming and costly initial calibration, while existing auto-calibration methods are not reliable for long-term operation under varying conditions.
Innovation Solution
A method for auto-calibration of stereo cameras using Structure-from-Motion (SfM) techniques, where a processor captures multiple images from stereo cameras during vehicle motion, computes world coordinates, and solves for camera parameters, including radial distortion, to generate a robust depth map, thereby reducing the need for initial calibration and maintaining long-term accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If off-line calibration with known target is used, then calibration accuracy is improved, but time consumption and cost increase
Solution Approach 1:
The system performs self-calibration by using naturally occurring features in the scene rather than requiring external calibration targets. The stereo camera system automatically identifies and uses environmental features (corners, edges, intersections) to compute its own extrinsic parameters, eliminating the need for manual calibration procedures with checkerboards or other known targets.
Solution Approach 2:
The invention extracts calibration information from natural scene features rather than requiring separate calibration equipment. By identifying corners, edges, and intersections in the environment, the system extracts sufficient geometric constraints to solve for camera parameters without removing or adding external calibration artifacts.
2Measurement precision
If off-line calibration with known target is used, then calibration accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The stereo camera system calibrates itself using processing units already present in the system. The calibration algorithm runs on the existing image processing hardware, utilizing naturally occurring scene features rather than requiring additional calibration equipment, targets, or specialized tools.
Solution Approach 2:
The same image processing pipeline used for normal stereo vision tasks is also used for calibration. The system performs dual functionality: regular depth estimation and self-calibration, using the same feature detection and matching algorithms without requiring separate calibration hardware or procedures.
3Measurement precision
If initial calibration is performed, then depth estimation accuracy is improved, but production time and cost increase
Solution Approach 1:
The stereo camera system performs automatic self-calibration during normal operation without requiring factory calibration procedures. This eliminates production line calibration steps, allowing cameras to be installed and immediately functional, thereby increasing manufacturing throughput and reducing labor costs.
Solution Approach 2:
The system performs calibration automatically during its first operational phase rather than requiring pre-calibration. The self-calibration process occurs naturally during initial use with real driving scenes, eliminating the need for time-consuming factory calibration procedures while ensuring accurate depth estimation from the start.
4Ease of operation
If auto-calibration is implemented, then maintenance requirements are reduced, but reliability under varying conditions may worsen
Solution Approach 1:
The calibration parameters are made dynamic and adaptable rather than fixed. The system continuously monitors feature matching quality and can re-calibrate when environmental conditions change, allowing the calibration state to adapt to varying temperatures, lighting, and scene characteristics while maintaining operational simplicity.
Data Source
AI summary
Auto-calibration of stereo cameras installable behind the windshield of a host vehicle and oriented to view the environment through the windshield. Multiple first image points are located of one of the first images captured from the first camera at a first time and matched with first image points of at least one other of the first images captured from the first camera at a second time to produce pairs of corresponding first image points respectively in the first images captured at the different times. World coordinates are computed from the corresponding first image points. Second image points in the second images captured from the second camera are matched to at least a portion of the first image points. The world coordinates as determined from the first camera are used, to solve for camera parameters of the second camera from the matching second image points of the second camera.