Multi-Camera Calibration via Triangulation and Eigenvalue Minimization
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Solution Overview
Problem
Traditional methods for generating calibration data in materials handling facilities with multiple cameras are labor-intensive, prone to errors, and costly, especially as the number of cameras increases, often requiring prior knowledge of the environment and the use of calibration targets or rigid frames that restrict camera placement and do not account for variations.
Innovation Solution
A system that determines calibration data for multiple cameras without prior knowledge of the environment geometry, without human intervention, using image data from cameras to identify features and calculate triangulation matrices, allowing for accurate translation and rotation determination of each camera, and is computationally efficient to scale to large numbers of cameras.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration methods using calibration targets or rigid frames are used, then calibration accuracy can be maintained, but device complexity and setup time increase significantly
Solution Approach 1:
The patent extracts the calibration target from the physical environment and replaces it with virtual markers generated through image processing. Instead of requiring physical calibration objects, the system identifies and tracks natural features in the environment through camera images, eliminating the need for specialized calibration equipment while maintaining accuracy.
Solution Approach 2:
The patent replaces the mechanical calibration system (rigid frames and physical targets) with a computational approach using triangulation matrices and image processing. The calibration is achieved through mathematical calculations based on image data from multiple cameras, substituting physical mechanical structures with algorithmic processing.
2Area of stationary object
If the number of cameras is increased to improve coverage, then monitoring capability improves, but calibration time and computational resources increase
Solution Approach 1:
The patent segments the calibration process into independent per-camera calibration operations. Each camera is calibrated individually using the same triangulation methodology, allowing parallel processing and reducing overall calibration time. The system processes each camera's image data independently to determine its translation and rotation parameters.
Solution Approach 2:
The system performs self-calibration by automatically identifying features in the environment and computing calibration parameters without human intervention. The automated feature detection and triangulation process eliminates manual calibration steps, enabling the system to scale to multiple cameras without proportionally increasing calibration time.
3Stability of the object's composition
If rigid frames are used to mount cameras, then camera position stability is ensured, but adaptability of camera placement is reduced
Solution Approach 1:
The patent transitions from static rigid frame mounting to dynamic flexible placement by using software-based calibration that can accommodate any camera position. The triangulation methodology adapts to whatever camera configuration is used, allowing cameras to be mounted on flexible booms or movable structures rather than fixed rigid frames, thereby improving adaptability while maintaining stability through computational correction.
Data Source
AI summary
A plurality of cameras obtain images in an environment. Calibration data about the translation and rotation of the cameras is used to support functionality such as determining a location of an object that appears in those images. To determine the calibration data, images are processed to determine features produced by points in the environment. A cluster is designated that includes the images of the same point as viewed from at least some of the cameras. A triangulation matrix and back-projection error are calculated for each cluster. These triangulation matrices and back-projection errors are then perturbed to find the translation and rotation that minimize eigenvalues and eigenvectors representative of the feature. The eigenvalues and eigenvectors are then used to determine the rotation and translation of the respective cameras with respect to an origin.