Hand-Eye Calibration
Introduction
Hand-eye calibration is used to relate what the camera (“eye”) sees to where the robot arm (“hand”) moves.
In a nutshell:
Eye-in-hand calibration is a process for determining the relative position and orientation of a robot-mounted camera with respect to the robot’s end-effector. It is usually done by capturing a set of images of a static object of known geometry with the robot arm located in a set of different positions and orientations.
Eye-to-hand calibration is a process for determining the position and orientation of a statically mounted camera with respect to the robot’s base frame. It is usually done by placing an object of known geometry in the robot’s gripper. Followed by taking a series of images of it in a set of different positions and orientations.
Further Reading
For a detailed description of the topic, please read the following pages:
- System Configurations
- Hand-Eye Calibration Problem
- Hand-Eye Calibration Solution
- Calibration Object
- How To Get Good Quality Data On Zivid Calibration Board
- Hand-Eye Calibration Process
- Cautions And Recommendations
- Hand-Eye Calibration Residuals
- How To Use The Result Of Hand-Eye Calibration
- UR5 Robot + Python: Generate Dataset and perform Hand-Eye Calibration
Version History
SDK |
Changes |
|---|---|
2.6.0 |
Improved robustness of the checkerboard detection with regards to Blooming - Bright Spots in the Point Cloud. |
2.4.0 |
The hand-eye calibration method is updated to improve accuracy, leading to roughly 50% improvement in translational residuals at a cost of slightly increased rotational residual. |
2.3.0 |
Improved robustness of the checkerboard detection. |
2.2.0 |
Support is added for the new line of official Zivid calibration boards, the first of which is the ZVD-CB01 (7x8 30 mm). |
2.0.0 |
Hand-eye API is changed, part of the |
1.6.0 |
Hand-eye calibration API is added. |