Stereo Camera Calibration Using Motion Grids for Stable Depth
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Solution Overview
Problem
Stereo cameras require periodic recalibration due to sensitivity to mechanical and thermal variations, which current methods, involving human interaction or noisy measurements, often deteriorate depth quality and are inefficient.
Innovation Solution
An autonomous stereo camera calibration system that performs calibration automatically without human intervention, using image processing units to stabilize calibration through Kalman filters, epipolar geometry, and safety metrics to ensure accurate depth estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual recalibration is performed, then calibration precision can be maintained, but operational continuity is interrupted and time is lost
Solution Approach 1:
The system performs autonomous calibration without human intervention. The processor automatically determines motion grids from image pairs, extracts matching points, calculates relative orientations, and updates calibration parameters. This self-service approach eliminates the need for manual recalibration operations while maintaining calibration precision.
Solution Approach 2:
The calibration process continues without interrupting camera operation. The system processes image pairs as they are captured during normal operation, continuously updating calibration parameters in real-time. This ensures calibration precision is maintained while eliminating downtime associated with manual recalibration.
2Measurement precision
If calibration is performed offline, then measurement precision can be achieved, but productivity is reduced due to operational interruptions
Solution Approach 1:
The system performs preliminary calibration actions continuously in the background during normal operation. By pre-processing image pairs and updating calibration parameters before they are needed, the system maintains depth estimation quality without requiring dedicated calibration time that would reduce productivity.
Solution Approach 2:
The calibration system transitions from static offline calibration to dynamic online calibration. The processor continuously adapts calibration parameters based on real-time image data, allowing the camera to maintain both high measurement precision and productivity by performing calibration actions during normal operational cycles.
3Reliability
If robustness estimation is implemented, then false calibration corrections are reduced, but computational complexity increases
Solution Approach 1:
The system implements feedback mechanisms where the processor evaluates the quality of calibration updates using metrics such as inlier ratios and motion gradients. This feedback loop allows the system to reject unreliable calibration corrections while accepting valid ones, improving calibration reliability through automated quality assessment without requiring overly complex additional hardware.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed to calibrate a stereo camera. An example apparatus includes means for determining a motion grid between a first image and a second image captured by the stereo camera; means for determining a calibration value to calibrate the stereo camera based on a prior calibration value, a relative orientation between the first image and the second image based on the motion grid, and a metric indicative of calibration improvement; and means for estimating a depth based on the calibration value.