Stereo Image Discrepancy Detection for Smudged Lens Navigation
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Solution Overview
Problem
Autonomous vehicle navigation systems, such as UAVs, face challenges in capturing quality images due to optical discrepancies caused by dirt, smudges, or damage to image capture devices, which can lead to inaccurate navigation and safety issues.
Innovation Solution
A technique for detecting optical discrepancies in stereo image pairs by calculating photometric errors and generating a threshold map to identify persistent errors, followed by corrective actions such as image masking and automatic lens cleaning to ensure accurate navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If image capture devices are used to guide autonomous vehicle navigation, then navigation capability is enabled, but optical discrepancies from dirt, smudges, or damage cause inaccurate navigation
Solution Approach 1:
The system performs preliminary detection of optical discrepancies by comparing stereo image pairs before using the images for navigation. By calculating photometric errors and generating threshold maps in advance, the system identifies and masks unreliable image regions, preventing corrupted data from affecting navigation accuracy.
Solution Approach 2:
The system continuously monitors image quality by detecting optical discrepancies in real-time during operation. The feedback loop compares captured stereo images, identifies regions with high photometric errors, and adjusts navigation decisions or triggers lens cleaning based on the detected discrepancy levels, ensuring sustained navigation accuracy.
2Measurement precision
If stereo image pairs are processed to detect optical discrepancies, then image quality assessment is improved, but computational complexity increases
Solution Approach 1:
The system segments the image processing task by first computing photometric errors for corresponding pixels in stereo pairs, then separating pixels into reliable and unreliable categories based on threshold comparisons. This segmentation allows selective processing where only regions with optical discrepancies require corrective actions like masking or lens cleaning.
Solution Approach 2:
Instead of processing entire images uniformly, the system applies partial action by focusing computational resources only on pixels or regions exhibiting optical discrepancies. The threshold map identifies specific problem areas, allowing the system to apply corrective measures selectively rather than globally, reducing overall processing complexity.
3Reliability
If photometric error calculation is performed on all pixels, then detection completeness is improved, but processing time increases
Solution Approach 1:
The system segments pixels into reliable and unreliable groups based on photometric error thresholds, allowing selective processing. By identifying and isolating pixels with significant discrepancies, the system can focus detailed analysis only on problematic regions while using simplified processing for the majority of reliable pixels, reducing overall processing time.
Solution Approach 2:
The system performs partial photometric error calculation by applying full scrutiny only to pixels exceeding threshold values, while using faster approximation methods or skipping detailed analysis for pixels well within acceptable ranges. This selective approach maintains detection completeness for problematic areas while minimizing processing time for the bulk of the image data.
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).