Robot Camera Calibration Using 3D Pattern Positioning
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Solution Overview
Problem
Existing camera calibration methods for robot control often require manual intervention and are prone to inaccuracies due to image noise, especially when estimating multiple intrinsic camera parameters simultaneously, which affects the robot's ability to accurately position its arm based on camera images.
Innovation Solution
A method and system for automatic camera calibration involving a robot control system that performs camera calibration in multiple stages, first estimating a first intrinsic camera parameter and then refining it in subsequent stages, using an imaginary cube to distribute calibration pattern locations within the camera's field of view to capture diverse images with varying lens distortion, and determining transformation functions to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual camera calibration is performed, then the process is simple to implement, but the calibration accuracy is reduced due to human error and image noise
Solution Approach 1:
The system performs automatic camera calibration without manual intervention. The robot autonomously moves the calibration pattern to multiple positions and orientations within the camera field of view, and the control circuit automatically processes the captured images to determine intrinsic camera parameters and transformation functions, eliminating human error while maintaining operational simplicity
Solution Approach 2:
The patent replaces manual mechanical calibration operations with an automated system. The robot arm mechanically positions the calibration pattern according to pre-determined locations on an imaginary cube, and the control circuit computationally processes images to calculate calibration parameters, substituting human manual operations with automated mechanical and computational processes
2Measurement precision
If multiple intrinsic camera parameters are estimated simultaneously, then the calibration process is completed in one step, but the accuracy is reduced due to image noise and errors
Solution Approach 1:
The calibration process is divided into multiple sequential stages. In the first stage, the control circuit determines at least one intrinsic camera parameter from captured images. In subsequent stages, additional intrinsic parameters are determined using results from previous stages. This segmented approach reduces error propagation and improves overall accuracy compared to simultaneous estimation
Solution Approach 2:
The system performs preliminary calibration by determining at least one intrinsic camera parameter in an initial stage before proceeding to determine other parameters. This preliminary estimation provides a foundation for subsequent calculations, reducing the impact of image noise and errors on final calibration accuracy
3Measurement precision
If the calibration pattern is positioned at limited locations, then the calibration process is faster, but the calibration accuracy is reduced due to insufficient data coverage
Solution Approach 1:
The system utilizes a three-dimensional imaginary cube to distribute calibration pattern locations throughout the camera field of view. The robot positions the calibration pattern at multiple locations on and within this cube, including corner locations and intermediate positions, creating comprehensive spatial coverage. This 3-dimensional distribution provides diverse calibration data that improves accuracy without excessively increasing calibration time
Data Source
AI summary
A robot control system and a method for automatic camera calibration is presented. The robot control system includes a control circuit configured to determine all corner locations of an imaginary cube that fits within a camera field of view, and determine a plurality of locations that are distributed on or throughout the imaginary cube. The control circuit is further configured to control a robot arm to move a calibration pattern to the plurality of locations, and to receive a plurality of calibration images corresponding to the plurality of locations, and to determine respective estimates of intrinsic camera parameters based on the plurality of calibration images, and to determine an estimate of a transformation function that describes a relationship between a camera coordinate system and a world coordinate system. The control circuit is further configured to control placement of the robot arm based on the estimate of the transformation function.