Camera Calibration Pose Planning for Uniform Field Coverage
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Solution Overview
Problem
Current camera calibration methods often result in concentrated pose distributions, leading to incomplete capture of camera behavior, particularly lens distortion, as they focus on specific regions and orientations, missing diverse manifestations of camera behavior.
Innovation Solution
A computing system determines a range of pattern orientations and poses for a calibration pattern using an imaginary sphere to achieve a uniform distribution of poses within the camera's field of view, ensuring diverse locations and orientations to capture a broader range of camera behaviors, including lens distortion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration methods are used with fixed robot movement, then the calibration process is simple to operate, but the coverage of camera behavior is incomplete and calibration accuracy is reduced
Solution Approach 1:
The system performs self-calibration by automatically generating robot movement commands and capturing calibration images without continuous manual intervention. The computing system autonomously controls the robot to move through diverse poses and orientations, capturing comprehensive camera behavior data for accurate calibration.
Solution Approach 2:
The system varies multiple parameters including robot position, camera orientation, and calibration pattern placement to achieve diverse poses. By systematically changing these parameters across multiple calibration images, the system captures comprehensive camera behavior including lens distortion across different field of view regions.
2Reliability
If calibration focuses on specific regions and orientations, then the calibration process is faster and simpler, but the camera behavior characterization is incomplete
Solution Approach 1:
The system transitions from 2D calibration plane to 3D spatial distribution by utilizing robot arm movements in three-dimensional space. This enables calibration patterns to be captured at diverse positions, depths, and orientations, comprehensively characterizing camera behavior including peripheral distortion and perspective effects.
Solution Approach 2:
The system employs dynamic robot movement through automatically generated motion commands that transition the calibration pattern through multiple poses and orientations. This dynamic approach ensures comprehensive coverage of the camera field of view while maintaining calibration efficiency through systematic motion planning.
3Measurement precision
If diverse poses are used to capture comprehensive camera behavior, then calibration accuracy is improved, but the complexity of determining poses and controlling robot movement increases
Solution Approach 1:
The computing system performs multiple functions including pose determination, robot motion planning, image capture coordination, and calibration data processing. This multi-functional integration manages system complexity by consolidating diverse calibration tasks into a unified automated workflow.
Solution Approach 2:
The system uses feedback from captured calibration images to refine pose determination and adjust subsequent robot movement commands. This iterative feedback mechanism ensures accurate calibration while managing complexity through adaptive control based on actual captured data.
Data Source
AI summary
A method and system for determining poses for camera calibration is presented. The system determines a range of pattern orientations for performing the camera calibration, and determines a surface region on a surface of an imaginary sphere, which represents possible pattern orientations for the calibration pattern. The system determines a plurality of poses for the calibration pattern to adopt. The plurality of poses may be defined by respective combinations of a plurality of respective locations within the camera field of view and a plurality of respective sets of pose angle values. Each set of pose angle values of the plurality of respective sets may be based on a respective surface point selected from within the surface region on the surface of the imaginary sphere. The system outputs a plurality of robot movement commands based on the plurality of poses that are determined.