Planar Scene Camera Localization Without Checkerboard Calibration
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Solution Overview
Problem
Traditional camera calibration and localization methods for multi-camera systems are labor-intensive, time-consuming, and impractical for dynamic environments, requiring controlled setups and disruptive checkerboard patterns, which hinder flexibility and efficiency in setups like sports venues and retail stores.
Innovation Solution
A camera localization approach that eliminates the need for initial calibration by using keypoint matching models and homography computations to determine camera parameters directly from planar scene data, allowing seamless integration of additional cameras without manual calibration patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional checkerboard pattern calibration is used, then camera parameters can be determined, but setup time and labor requirements increase significantly
Solution Approach 1:
The patent extracts and removes the time-consuming checkerboard pattern placement and manual calibration steps from the camera setup process. Instead of requiring physical calibration patterns, the system directly processes images from multiple cameras to determine parameters, eliminating the preparatory calibration phase entirely.
Solution Approach 2:
The system enables cameras to self-localize and self-calibrate by automatically processing images captured from multiple viewpoints. The computational algorithm autonomously determines camera parameters and positions without requiring manual intervention or pre-placed calibration objects, allowing the system to service itself during setup.
2Measurement precision
If traditional calibration methods are used, then accurate camera parameters are obtained, but the process becomes labor-intensive and complex
Solution Approach 1:
The patent replaces the mechanical calibration process (physical checkerboard patterns, manual camera positioning, and hands-on adjustment) with a computational system. Images captured by multiple cameras are processed through algorithms that automatically compute camera parameters, substituting physical manipulation with digital computation.
Solution Approach 2:
The system changes the approach from determining camera parameters through physical measurement of calibration patterns to computing parameters directly from image data. By transforming the problem from physical calibration to computational analysis, the system simplifies the process while maintaining accuracy.
3Measurement precision
If checkerboard patterns are deployed for calibration, then camera localization is achieved, but operational disruptions occur in dynamic environments
Solution Approach 1:
The patent removes the disruptive checkerboard patterns from the operational environment. By eliminating the need to place and maintain physical calibration objects in dynamic spaces like sports venues or retail stores, the system avoids obstructing normal activities while still achieving accurate camera localization.
Solution Approach 2:
The system performs camera calibration and localization simultaneously during the initial image capture phase, before any operational disruptions could occur. By computing all necessary parameters from the first set of multi-camera images, the system eliminates subsequent calibration interruptions that would disrupt dynamic environments.
4Reliability
If manual calibration procedures are followed, then camera systems can be set up, but integration of additional cameras becomes time-consuming
Solution Approach 1:
The patent creates a universal calibration approach that works for any number of cameras in the system. The same computational algorithm handles both initial camera setup and subsequent addition of new cameras, eliminating the need for separate calibration procedures and enabling seamless integration of additional cameras at any time.
Solution Approach 2:
The system maintains continuous operational capability by allowing cameras to be added and integrated without breaking the calibration state. New cameras can be incorporated into the existing system through the same image-based computational process, maintaining uninterrupted functionality and enabling rapid system expansion.
Data Source
AI summary
Systems and techniques are described for localizing a camera. An example method includes generating first image data comprising a first image plane representing the planar scene from a first point-of-view. The example includes generating second image data comprising the second image plane representing the planar scene from a second point-of-view. The example includes identifying a plurality of keypoints of the planar scene that is visible in the first image plane and the second image plane. The example includes matching the plurality of keypoints of a planar scene to a first pixel of a first image plane and a second pixel of a second image plane. The example includes generating a homography relating the planar scene, the first image plane, and the second image plane. The example includes generating, based on the homography, at least one of a calibration parameter or a localization parameter for an unlocalized camera.