Runtime Camera Miscalibration Diagnosis via Alignment Statistics
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Solution Overview
Problem
Current machine vision systems face challenges in accurately determining camera calibration during runtime, especially in adverse environments, leading to suboptimal performance due to changes in camera position and lens distortions, which can be difficult to trace and require costly downtime for recalibration.
Innovation Solution
A system and method for runtime self-diagnosis of camera miscalibration using historical statistics of object alignment measurements, allowing for frequent validation without stopping production, by comparing measurements from multiple cameras to determine if recalibration is needed and generating new extrinsic calibration parameters as necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If camera calibration is performed frequently to ensure accuracy, then measurement precision is improved, but productivity deteriorates due to system downtime
Solution Approach 1:
The system performs preliminary calibration during system setup and stores these parameters. During runtime, it uses these pre-established parameters for validation without requiring full recalibration, thus maintaining accuracy while minimizing downtime.
Solution Approach 2:
The system creates a copy of the calibration target image during runtime and compares it with the stored reference image. This copying approach allows validation without physical intervention or system shutdown, preserving both accuracy and productivity.
2Productivity
If camera calibration is performed infrequently to maintain productivity, then productivity is improved, but measurement precision deteriorates due to drift
Solution Approach 1:
The system continuously monitors camera calibration status during runtime by comparing current images with reference images. This feedback mechanism detects drift early and triggers alerts before accuracy deteriorates significantly, allowing maintenance without full shutdowns.
Solution Approach 2:
The system performs self-validation of calibration status using automated image comparison algorithms. This self-monitoring capability detects calibration drift without external intervention, maintaining precision while minimizing impact on productivity.
3Productivity
If validation procedures are performed during runtime without stopping production, then productivity is improved, but measurement precision deteriorates due to lack of comprehensive calibration checks
Solution Approach 1:
The system extracts only the essential validation information from runtime images—specifically comparing key feature points and alignment metrics—rather than performing complete calibration procedures. This extraction approach maintains sufficient precision while enabling continuous operation.
Data Source
AI summary
This invention provides a system and method for runtime determination (self-diagnosis) of camera miscalibration (accuracy), typically related to camera extrinsics, based on historical statistics of runtime alignment scores for objects acquired in the scene, which are defined based on matching of observed and expected image data of trained object models. This arrangement avoids a need to cease runtime operation of the vision system and/or stop the production line that is served by the vision system to diagnose if the system's camera(s) remain calibrated. Under the assumption that objects or features inspected by the vision system over time are substantially the same, the vision system accumulates statistics of part alignment results and stores intermediate results to be used as indicator of current system accuracy. For multi-camera vision systems, cross validation is illustratively employed to identify individual problematic cameras. The system and method allows for faster, less-expensive and more-straightforward diagnosis of vision system failures related to deteriorating camera calibration.