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Automated, hardware-independent Hand-Eye Calibration for ROS2
| Date | Stars |
|---|---|
| 2026-07-24 | 284 |
| 2026-07-25 | 284 |
| 2026-07-28 | 284 |
| 2026-07-30 | 284 |
| 2026-08-06 | 284 |
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# easy_handeye2: automated, hardware-independent Hand-Eye Calibration for ROS2
<img src="docs/img/eye_on_base_ndi_pic.png" width="345"/> <img src="docs/img/05_calibrated_rviz.png" width="475"/>
This package provides functionality and a GUI to:
- **sample** the robot position and tracking system output via `tf`,
- **compute** the eye-on-base or eye-in-hand calibration matrix through the OpenCV library's hand-eye calibration algorithms (e.g. Tsai-Lenz),
- **store** the result of the calibration,
- **publish** the result of the calibration procedure as a `tf` transform at each subsequent system startup,
- **evaluate** the accuracy of the resulting calibration matrix,
- (optional) automatically **move** a robot around a starting pose via `MoveIt!` to acquire the samples.
The intended result is to make it easy and straightforward to perform the calibration, and to keep it up-to-date throughout the system.
Two launch files are provided to be run, respectively to perform the calibration and check its result.
A further launch file can be integrated into your own launch files, to make use of the result of the calibration in a transparent way:
if the calibration is performed again, the updated result will be used without further action required.
You can try out this software in a simulator, through the
[easy_handeye2_demo package](https://github.com/marcoesposito1988/easy_handeye2_demo). This package also serves as an
example for integrating `easy_handeye2` into your own launch scripts.
This is a port of [easy_handeye](https://github.com/IFL-CAMP/easy_handeye) to ROS2.
## News
- version 0.5.0
- port to ROS2
- addition of rqt evaluator script
## Use Cases
If you are unfamiliar with Tsai's hand-eye calibration [1], it can be used in two ways:
- **eye-in-hand** to compute the static transform between the reference frames of
a robot's hand effector and that of a tracking system, e.g. the optical frame
of an RGB camera used to track AR markers. In this case, the camera is
mounted on the end-effector, and you place the visual target so that it is
fixed relative to the base of the robot; for example, you can place an AR marker on a table.
- **eye-on-base** to compute the static transform from a robot's base to a tracking system, e.g. the
optical frame of a camera standing on a tripod next to the robot. In this case you can attach a marker,
e.g. an AR marker, to the end-effector of the robot.
A relevant example of an eye-on-base calibration is finding the position of an RGBD camera with respect to a robot for object collision avoidance, e.g. [with MoveIt!](http://docs.ros.org/indigo/api/moveit_tutorials/html/doc/pr2_tutorials/planning/src/doc/perception_configuration.html): an [example launch file](docs/example_launch/ur5_kinect_calibration.launch) is provided to perform this common task between an Universal Robot and a Kinect through aruco. eye-on-hand can be used for [vision-guided tasks](https://youtu.be/nBTflbxYGkI?t=24s).
The (arguably) best part is, that you do not have to care about the placement of the auxiliary marker
(the one on the table in the eye-in-hand case, or on the robot in the eye-on-base case). The algorithm
will "erase" that transformation out, and only return the transformation you are interested in.
eye-on-base | eye-on-hand
:-------------------------:|:-------------------------:
 | 
## Getting started
- clone this repository into your catkin workspace:
```
cd ~/easy_handeye2_ws/src # replace with path to your workspace
git clone https://github.com/marcoesposito1988/easy_handeye2
```
- satisfy dependencies
```
cd .. # now we are inside ~/easy_handeye2_ws
rosdep install -iyr --from-paths src
```
- build
```
colcon build
```
## Usage
Two launch files, one for computing and one for publishing the calibration respectively,
are provided to be included in your own. The default aExcerpt of 11,281 characters
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