Asynchronous Camera Image Synchronization by Flatness Scoring
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Solution Overview
Problem
Existing systems for autonomous vehicle parking lack efficient methods to synchronize asynchronous cameras, necessitating specialized equipment and posing logistical and economic challenges, especially in parking large vehicles like freight trucks.
Innovation Solution
A method for synchronizing images from vehicle-mounted and infrastructure cameras using iterative comparison, flatness scoring, and triangulation techniques, eliminating the need for additional equipment by leveraging existing camera systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If asynchronous cameras are used for monitoring, then device complexity is reduced, but image synchronization accuracy deteriorates
Solution Approach 1:
The system uses the cameras' own captured images to perform synchronization without external signals. The self-service mechanism compares images from multiple cameras to identify corresponding features and calculate time offsets, eliminating the need for dedicated synchronization equipment while maintaining accuracy.
Solution Approach 2:
The patent introduces an image comparison process as an intermediary mechanism between asynchronous cameras. By using feature matching and flatness score calculation on captured images, the system mediates the synchronization problem without requiring direct hardware synchronization or additional signaling equipment.
2Measurement precision
If specialized synchronization equipment is used, then image synchronization accuracy is improved, but device complexity increases
Solution Approach 1:
The system uses the cameras' own captured images to perform synchronization without external signals. The self-service mechanism compares images from multiple cameras to identify corresponding features and calculate time offsets, eliminating the need for dedicated synchronization equipment while maintaining accuracy.
Solution Approach 2:
The patent replaces mechanical/hardware synchronization mechanisms with a software-based image processing approach. Instead of using dedicated synchronization signals or hardware triggers, the system uses computational methods including feature extraction, ORB matching, and flatness score calculation to achieve synchronization.
3Device complexity
If traditional external camera systems are used, then device complexity is reduced, but operational support capability deteriorates
Solution Approach 1:
The system makes the camera system multi-functional by enabling it to perform both traditional monitoring tasks and operational support functions. Through image synchronization and fusion, the cameras provide comprehensive environmental awareness, route generation assistance, and docking support, transforming single-function monitoring devices into versatile operational assistance tools.
Solution Approach 2:
The patent merges data from multiple camera sources (vehicle-mounted and infrastructure cameras) to create a unified environmental model. By combining images and synchronizing them in time and space, the system creates a comprehensive view that supports multiple operational functions simultaneously, enhancing the overall capability beyond what individual cameras can provide.
4Adaptability or versatility
If multiple cameras are integrated for comprehensive monitoring, then operational support capability is improved, but image synchronization difficulty increases
Solution Approach 1:
The patent introduces an image comparison process as an intermediary mechanism between asynchronous cameras. By using feature matching and flatness score calculation on captured images, the system mediates the synchronization problem without requiring direct hardware synchronization or additional signaling equipment.
Solution Approach 2:
The patent replaces mechanical/hardware synchronization mechanisms with a software-based image processing approach. Instead of using dedicated synchronization signals or hardware triggers, the system uses computational methods including feature extraction, ORB matching, and flatness score calculation to achieve synchronization.
Data Source
AI summary
Image synchronization systems and methods enable seamless integration of advanced triangulation and synchronization of different cameras that traditionally operate asynchronously. In various embodiments, this is accomplished by performing iterative steps for pairs of images that include comparing images obtained from two cameras, calculating a flatness score indicative of an error, and selecting a pair of images that is associated with the lowest error to identify synchronized images.