Hand-Eye Calibration With Robot Pose Error Modeling
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Solution Overview
Problem
Conventional hand-eye calibration methods for camera-guided apparatuses assume error-free robot poses, leading to inaccuracies and requiring expensive, time-consuming high-precision measurement instruments, while also failing to account for the absolute accuracy of robots, which is crucial for applications requiring precise transformations between robot and camera coordinate systems.
Innovation Solution
A method that statistically models the inaccuracy of robot poses and camera parameters, allowing for simultaneous calibration of internal camera parameters and providing error-corrected robot poses, enabling accurate hand-eye calibration without the need for known 3D points or high-precision instruments, and supporting both calibration object-based and self-calibration approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional hand-eye calibration methods are used, then the calibration process is simple, but the accuracy of hand-eye pose calculation deteriorates due to unmodeled robot inaccuracy
Solution Approach 1:
The patent changes the mathematical parameters of the calibration method by introducing statistical error models for robot poses and camera parameters. Instead of using deterministic equations, the patent employs probabilistic parameter estimation that accounts for robot inaccuracy, thereby improving hand-eye pose calculation accuracy without requiring complex additional hardware
Solution Approach 2:
The patent replaces the need for high-precision mechanical measurement instruments with a computational approach. By substituting physical measurement tools with statistical modeling and optimization algorithms, the patent achieves high accuracy while avoiding the complexity and cost of precision mechanical devices
2Measurement precision
If high-precision measurement instruments are used, then the accuracy of robot calibration is improved, but the cost and time consumption increase
Solution Approach 1:
The patent uses inexpensive calibration objects (such as standard calibration patterns or markers) instead of expensive high-precision measurement instruments. These simple calibration objects can be easily manufactured and replaced, providing accurate calibration results without the high cost and time requirements of precision measurement equipment
Solution Approach 2:
The patent enables the robot system to perform self-calibration by modeling and correcting its own errors through statistical analysis of calibration data. The system uses its own camera and motion data to compute error corrections, eliminating the need for external high-precision measurement instruments and reducing calibration time
3Measurement precision
If robot inaccuracy is not explicitly modeled, then the calibration process is simpler, but the hand-eye calibration result deteriorates
Solution Approach 1:
The patent transforms the calibration problem by changing from deterministic parameter estimation to probabilistic parameter estimation. By modeling robot poses and camera parameters as random variables with associated error distributions, the patent improves calibration accuracy while keeping the computational framework manageable through standardized statistical methods
Solution Approach 2:
The patent introduces statistical error models as intermediary representations between the physical robot system and the hand-eye calibration calculation. These error models serve as mediators that capture robot inaccuracy without requiring direct measurement of every error source, simplifying the overall system while improving accuracy
4Measurement precision
If simultaneous calibration of camera parameters is performed, then the overall calibration accuracy is improved, but the computational complexity increases
Solution Approach 1:
The patent merges the hand-eye calibration process with camera parameter calibration into a unified statistical optimization framework. By combining these two calibration tasks simultaneously and modeling their errors together, the patent achieves improved overall accuracy while managing computational complexity through integrated parameter estimation
Data Source
AI summary
The invention describes a generic framework for hand-eye calibration of camera-guided apparatuses, wherein the rigid 3D transformation between the apparatus and the camera must be determined. An example of such an apparatus is a camera-guided robot.