Imaging System Self-Calibration via Epipolar Feature Matching
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Solution Overview
Problem
Traditional imaging systems require recalibration at dedicated facilities due to changes from vibrations, temperature, and other environmental factors, leading to cumbersome and costly recalibration processes, limiting their widespread adoption.
Innovation Solution
A method and system that allows for online recalibration of imaging systems using feature correspondences and depth maps, enabling detection of miscalibration events and recalibration during operation without the need for factory recalibration, using a computing system to process images and determine calibration parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional imaging systems are calibrated at dedicated calibration facilities, then calibration accuracy is maintained, but system complexity and operational burden increase due to required returns to calibration facilities
Solution Approach 1:
The imaging system performs self-calibration by detecting recalibration events through monitoring correspondence map quality and automatically executing recalibration procedures when degradation is detected, eliminating the need for external calibration facilities and reducing operational burden while maintaining calibration accuracy
Solution Approach 2:
The system continuously monitors the quality of correspondence maps and calibration parameters during operation, providing feedback that triggers automatic recalibration when degradation thresholds are exceeded, thereby maintaining measurement precision without requiring manual intervention or return to calibration facilities
2Stability of the object's composition
If heavy rigid housing is used to mitigate camera alignment changes, then system stability improves, but device portability and ease of installation deteriorate
Solution Approach 1:
The imaging system automatically detects alignment changes through correspondence map analysis and performs self-recalibration when miscalibration events are detected, eliminating the need for heavy rigid housing to prevent alignment changes and thereby improving portability while maintaining stability
Solution Approach 2:
The system dynamically adapts to alignment changes by continuously monitoring calibration quality and automatically adjusting calibration parameters when changes are detected, replacing static prevention methods (heavy housing) with dynamic compensation that improves portability while maintaining stability
3Measurement precision
If factory recalibration is required, then initial calibration accuracy is ensured, but time loss and operational downtime increase
Solution Approach 1:
The system performs preliminary calibration at the factory to establish accurate initial parameters, then continuously monitors for recalibration needs during operation, enabling quick on-demand recalibration that minimizes downtime compared to requiring returns to calibration facilities
Solution Approach 2:
The imaging system performs automatic self-recalibration during operation when degradation is detected, eliminating the need for manual intervention and extended downtime associated with factory recalibration, thereby reducing time loss while maintaining calibration accuracy
Data Source
AI summary
A system and/or method for imaging system calibration, including: determining a dense correspondence map matching features in a first image to features in a second image, and determining updated calibration parameters associated with the image acquisition system based on the matching features.