Stereo Disparity Density Control for Mobile Machine Sensor Impairment
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Solution Overview
Problem
Existing machine control systems using stereo disparity for mobile machines are ineffective in maintaining operation when sensor impairments occur, often leading to system slowdown or failure due to reliance on impaired camera images without improving image quality or adapting control strategies.
Innovation Solution
A control system that includes sensors to capture images, generate stereo images, compute disparity maps, and adjust operations based on disparity density thresholds, allowing for autonomous control and adaptive strategies when image quality is low, and potentially utilizing high-quality image data from other machines to improve visibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If the system relies on stereo disparity images for machine control, then automation and efficiency are improved, but when sensors are impaired (rain, snow, dust, fog), the control system becomes unreliable or stops working
Solution Approach 1:
The system continuously monitors disparity density as feedback to assess image quality. When disparity density falls below the threshold indicating sensor impairment, the system adjusts control strategies accordingly, maintaining reliability under varying conditions while preserving automation
Solution Approach 2:
The control system dynamically adapts its operation based on real-time disparity density measurements. When sensors are impaired, the system transitions from relying on stereo disparity to alternative control methods, maintaining automation while adjusting to changing environmental conditions
2Reliability
If the system ignores images when disparity density is low, then control reliability is maintained, but productivity decreases as the system slows down or stops working
Solution Approach 1:
The system changes the operational parameter from binary (process/ignore) to continuous (disparity density threshold). By comparing disparity density against a threshold, the system can make nuanced decisions about when to use alternative control strategies, maintaining both reliability and productivity
Solution Approach 2:
The disparity density threshold acts as an intermediary metric that bridges image quality assessment and control decision-making. This intermediary allows the system to translate sensor impairment detection into appropriate control strategy adjustments without completely halting operation
3Measurement precision
If more features are matched between images to improve disparity density, then measurement precision is improved, but the complexity of image processing increases
Solution Approach 1:
The system replaces complex mechanical feature-matching processes with a simpler computational approach based on disparity density calculation. This substitution maintains measurement precision while reducing processing complexity through more efficient algorithms
Data Source
AI summary
A control system for a mobile machine is disclosed. The control system may have a first sensor mounted on the mobile machine and configured to capture a first image of a region near the mobile machine, a second sensor mounted on the mobile machine and configured to capture a second image of the region, and a controller in communication with the first and second sensors. The controller may be configured to generate a stereo image from the first and second images, compute a disparity map of the stereo image, and generate an output to affect operation of the machine when a density of the disparity map is less than a threshold density.