Stereo Image Discrepancy Detection for Reliable Visual Navigation
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Solution Overview
Problem
Optical discrepancies in captured images, such as those caused by dirt, smudges, or calibration errors, can impede the quality of images used for autonomous vehicle navigation, leading to potential navigation errors.
Innovation Solution
Techniques for detecting optical discrepancies by calculating photometric errors between stereo image pairs and generating a threshold map to identify persistent errors, followed by generating an image mask or notification for corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If image capture devices are used for autonomous navigation, then navigation capability is enabled, but optical discrepancies from dirt, smudges, or calibration errors degrade image quality and cause navigation errors
Solution Approach 1:
The system performs preliminary detection of optical discrepancies by comparing stereo image pairs before navigation decisions are made. By identifying dirt, smudges, or calibration errors in advance through photometric error analysis, the system can flag affected regions and compensate for them, preventing navigation errors before they occur.
Solution Approach 2:
The system continuously monitors image quality by calculating photometric errors between stereo pairs and provides feedback about optical discrepancies. This feedback loop enables real-time detection of lens contamination or calibration issues, allowing the navigation system to adjust its processing or alert operators to maintain reliable navigation despite deteriorating optical conditions.
2Measurement precision
If photometric error calculation is performed between stereo image pairs, then optical discrepancies are detected, but processing complexity and computational load increase
Solution Approach 1:
Instead of uniformly processing all pixels in stereo image pairs, the system focuses photometric error calculations on specific regions or features that are most indicative of optical discrepancies. This localized approach maintains high detection accuracy for lens contamination and calibration errors while reducing overall computational load by processing only critical areas.
Solution Approach 2:
The system introduces an intermediary processing layer that pre-identifies potential discrepancy regions in stereo images before performing detailed photometric error analysis. This intermediary step filters out normal variations and focuses computational resources on areas where optical discrepancies are most likely to occur, simplifying the overall processing architecture while maintaining detection precision.
3Reliability
If real-time detection of optical discrepancies is implemented, then navigation reliability is maintained, but processing time and computational resources are consumed
Solution Approach 1:
The system performs photometric error calculations continuously on stereo image pairs as they are captured, maintaining constant monitoring of optical conditions without interrupting the navigation workflow. This continuous detection approach ensures real-time reliability by immediately identifying optical discrepancies, while the streamlined processing pipeline minimizes time loss by operating in parallel with image capture.
Solution Approach 2:
The detection system is integrated directly into the autonomous navigation pipeline, allowing it to self-monitor and self-diagnose optical issues without external intervention. The system automatically compares stereo pairs, detects discrepancies, and flags affected regions without requiring separate processing steps, thereby maintaining navigation reliability while minimizing additional processing time through efficient resource utilization.
Data Source
AI summary
Embodiments are described for detecting optical discrepancies associated with image capture analyzing pixels in multiple images corresponding to common points of reference in a physical environment. In an embodiment, photometric error values are averaged over time to compute the mean error at each pixel. Once the estimate of the mean error has a sufficient number of updates above a specified value, the estimate is thresholded to provide a mask of any optical discrepancies occurring in the stereo pair of images. Applications include detecting optical discrepancies in images captured for use by a visual navigation system in guiding an autonomous vehicle (e.g., an unmanned aerial vehicle).