Camera Calibration Using Depth Information
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Solution Overview
Problem
Current camera calibration methods require physical calibration objects, are slow, unreliable, or require substantial processing capabilities, and do not support real-time 3-D information extraction or dynamic camera configurations, especially when dealing with multiple cameras.
Innovation Solution
The method employs depth information from cameras to determine the relative position and orientation of multiple imaging devices by comparing images of a commonly depicted object, using depth maps to optimize the calibration process without the need for physical calibration objects, and allows for dynamic camera configurations and real-time 3-D information extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical calibration objects are used for camera calibration, then calibration accuracy is improved, but the complexity of operation and setup increases
Solution Approach 1:
The system uses naturally occurring features in the scene (corners, edges, intersections) to perform self-calibration without requiring external calibration objects. The calibration process serves itself by utilizing the scene content that is already present, eliminating the need for separate calibration objects and reducing operational complexity.
Solution Approach 2:
The invention creates a virtual model of the scene with 3D points and uses this virtual representation to perform calibration. Instead of physically handling calibration objects, the system copies the essential geometric information from the scene into a virtual model that can be processed computationally.
2Reliability
If traditional camera calibration methods are used, then calibration reliability is improved, but processing time increases
Solution Approach 1:
The invention replaces traditional mechanical calibration approaches (physical calibration objects, manual setup) with a computational image processing system. By substituting mechanical calibration procedures with automated algorithms that process image data, the system achieves both reliability and speed without the time-consuming manual operations.
Solution Approach 2:
The system performs preliminary detection of feature points and creates a virtual scene model before the actual calibration computation. This preliminary organization of geometric information prepares the data in advance, making the subsequent calibration process faster and more reliable without requiring repeated iterations.
3Measurement precision
If conventional calibration methods are used, then calibration accuracy is improved, but processing power requirements increase
Solution Approach 1:
The invention extracts only the essential geometric features (corner points, intersections, edges) from the images and uses these extracted elements for calibration. By taking out only the necessary information rather than processing entire images or redundant data, the system maintains calibration accuracy while significantly reducing processing power requirements.
Solution Approach 2:
The calibration process is segmented into distinct stages: feature detection, virtual model creation, and calibration computation. This segmentation allows each stage to process only the relevant data for its specific task, improving efficiency and reducing overall processing power requirements while maintaining accuracy.
4Reliability
If standard calibration approaches are used, then calibration robustness is improved, but adaptability to dynamic scenes decreases
Solution Approach 1:
The invention creates a dynamic calibration system that can adapt to changing scene conditions. By using naturally occurring features rather than fixed calibration objects, the system can handle dynamic scenes where objects and features may move or change. The virtual model can be updated as new images are captured, maintaining robustness while adapting to dynamic environments.
Data Source
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AI summary
A method for determining the relative position and orientation of a first and a second imaging device by comparing the image of a commonly depicted object in a first and a second image of the first and the second imaging device respectively, wherein the first and the second imaging devices are adapted for providing first and second depth information respectively for the respective images and in which at least the first and the second depth information is used for the determination. Corresponding device are also disclosed.