2024.5.28.test1.0

# easy_handeye: automated, hardware-independent Hand-Eye Calibration for ROS1 <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 Tsai-Lenz algorithm implementation, - **store** the result of the calibration, - **publish** the result of the calibration procedure as a `tf` transform at each subsequent system startup, - (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_handeye_demo package](https://github.com/marcoesposito1988/easy_handeye_demo). This package also serves as an example for integrating `easy_handeye` into your own launch scripts. **NOTE:** a (development) ROS2 version of this package is available [here](https://github.com/marcoesposito1988/easy_handeye2) ## News - version 0.4.3 - documentation and bug fixes - version 0.4.2 - fixes for the freehand robot movement scenario - version 0.4.1 - fixed a bug that prevented loading and publishing the calibration - thanks to @lyh458! - version 0.4.0 - switched to OpenCV as a backend for the algorithm implementation - added UI element to pick the calibration algorithm (Tsai-Lenz, Park, Horaud, Andreff, Daniilidis) - version 0.3.1 - restored compatibility with Melodic and Kinetic along with Noetic - version 0.3.0 - ROS Noetic compatibility - added "evaluator" GUI to evaluate the accuracy of the calibration while running `check_calibration.launch` ## 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 ~/catkin_ws/src # replace with path to your workspace git clone https://github.com/IFL-CAMP/easy_handeye ``` - satisfy dependencies ``` cd .. # now we are inside ~/catkin_ws rosdep install -iyr --from-paths src ``` - build ``` catkin 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 arguments should be overridden to specify the correct tf reference frames, and to avoid conflicts when using multiple calibrations at once. The suggested integration is: - create a new `handeye_calibrate.launch` file, which includes the robot's and tracking system's launch files, as well as `easy_handeye`'s `calibrate.launch` as illustrated below in the next section "Calibration" - in each of your launch files where you need the result of the calibration, include `easy_handeye`'s `publish.launch` as illustrated below in the section "Publishing" ### Calibration For both use cases, you can either launch the `calibrate.launch` launch file, or you can include it in another launchfile as shown below. Either way, the launch file will bring up a calibration script. By default, the script will interactively ask you to accept or discard each sample. At the end, the parameters will be saved in a yaml file. #### eye-in-hand ```xml <launch> <!-- (start your robot's MoveIt! stack, e.g. include its moveit_planning_execution.launch) --> <!-- (start your tracking system's ROS driver) --> <include file="$(find easy_handeye)/launch/calibrate.launch"> <arg name="eye_on_hand" value="true"/> <!-- you can choose any identifier, as long as you use the same for publishing the calibration --> <arg name="namespace_prefix" value="my_eih_calib"/> <!-- fill in the following parameters according to your robot's published tf frames --> <arg name="robot_base_frame" value="/base_link"/> <arg name="robot_effector_frame" value="/ee_link"/> <!-- fill in the following parameters according to your tracking system's published tf frames --> <arg name="tracking_base_frame" value="/optical_origin"/> <arg name="tracking_marker_frame" value="/optical_target"/> </include> </launch> ``` #### eye-on-base ```xml <launch> <!-- (start your robot's MoveIt! stack, e.g. include its moveit_planning_execution.launch) --> <!-- (start your tracking system's ROS driver) --> <include file="$(find easy_handeye)/launch/calibrate.launch"> <arg name="eye_on_hand" value="false"/> <arg name="namespace_prefix" value="my_eob_calib"/> <!-- fill in the following parameters according to your robot's published tf frames --> <arg name="robot_base_frame" value="/base_link"/> <arg name="robot_effector_frame" value="/ee_link"/> <!-- fill in the following parameters according to your tracking system's published tf frames --> <arg name="tracking_base_frame" value="/optical_origin"/> <arg name="tracking_marker_frame" value="/optical_target"/> </include> </launch> ``` #### Moving the robot A GUI for automatic robot movement is provided by the `rqt_easy_handeye` package. Please refer to [its documentation](rqt_easy_handeye/README.md). This is optional, and can be disabled in both aforementioned cases with: ```xml <launch> <include file="$(find easy_handeye)/launch/calibrate.launch"> <!-- other arguments, as described above... --> <arg name="freehand_robot_movement" value="true" /> </include> </launch> ``` It will then be the user's responsibility to make the robot publish its own pose into `tf`. Please check that the robot's pose is updated correctly in RViz before starting to acquire samples (the robot driver may not work while the teaching mode button is pressed, etc). The same applies to the validity of the samples. For the calibration to be found reliabl