documentation:vision:hand_eye_calibration [robotwiki]

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====== Hand-eye calibration with SkiROS ======
Hand-eye calibration is currently implemented in the [[https://git.cs.lth.se/robotlab/rvmi/vision_skills/-/blob/master/src/vision_skills/handeye_calibration/handeye_calibration.py|handeye_calibration_coordinator]] skill in the [[https://git.cs.lth.se/robotlab/rvmi/vision_skills|vision_skills]] repository.

Required world model elements:
  * **Arm** - the arm to which the camera is attached.
  * **Gripper** - the end-effector attached to the arm.
  * **Camera** - the camera to calibrate.
  * **CameraEstimate** - Estimate of camera position.
  * **ViewFrame** - the camera's view TF frame.
  * **Marker** - the AruCO marker.
  * **MarkerEstimate** - Estimate of marker's position.

Required world model relations:
  * **Arm** ''skiros:hasA'' **Gripper**
  * **Gripper** ''skiros:contain'' **Camera**
  * **Camera** ''skiros:spatiallyRelated'' **ViewFrame**
  * **CameraEstimate** ''scalable:estimateOf'' **Camera**
  * **MarkerEstimate** ''scalable:estimateOf'' **Marker**

The **Arm** needs the following properties set in the world model:
  * ''skiros:LinkedToFrameId''
  * ''skiros:MotionExe''
  * ''skiros:MoveItGroup'' (when using MoveIt implementation)
  * ''skiros:MoveItReferenceFrame'' (when using MoveIt implementation)
  * ''skiros:MoveItTCPLink'' (when using MoveIt implementation)

The **Camera** needs the following properties set in the world model:
  * ''skiros:DriverAddress''

The **Gripper** needs the following properties set in the world model:
  * ''skiros:SizeY'' (when using the Kuka implementation)

When running, the skill will update the position of **MarkerEstimate** and **CameraEstimate**.
The **Camera** position with be updated periodically based on **CameraEstimate**.
Finally, the skill will terminate when the CameraEstimate is considered converged.

====== To-Do list for Hand-Eye calibration skill ======
  * Investigate how to improve the half-sphere pose generator.
  * Investigate why the pose generator generates weird poses over time.
  * Investigate why the estimated camera pose is inaccurate.
  * Investigate whether the same skill can be re-used for fixed-camera calibration.
  * Add a parameter to allow the user to select which ArUco dictionary is used.