To make a robot-mounted camera’s observations agree with the robot’s motion, estimate the rigid transform between the camera and robot using paired robot poses and camera observations of a stationary target. In MoveIt’s documented workflow, that means choosing the right frames, checking camera inputs, collecting varied poses, solving the hand-eye calibration, and validating the resulting transform on the robot. Calibration is one part of a reliable teleoperation setup; it does not by itself address latency, network behavior, safety limits, or task-specific performance.
Choose the camera arrangement and frame roles
First establish how the camera is mounted. In an eye-in-hand setup, the camera is rigidly attached to the robot’s end effector. In an eye-to-hand setup, it is mounted rigidly relative to the robot base. MoveIt supports both arrangements, but its detailed tutorial describes the eye-in-hand workflow. The steps and frame choices below follow that workflow; adapt them to the documented configuration for your robot and software version. MoveIt’s Hand-Eye Calibration tutorial.
For eye-in-hand calibration, identify these frames by their physical meaning, not just their names:
- Camera optical frame: the sensor frame used for the camera’s observations. MoveIt cites ROS REP 103 for the optical-frame right-down-forward convention.
- End-effector link: the robot link rigidly attached to the camera.
- Target or object frame: the frame localized from the calibration pattern.
- Robot base frame: the reference in which the target must remain stationary during data collection.
Before collecting data, inspect the robot’s TF tree and verify the direction of each transform in the chain. A plausible-looking frame name does not prove that it represents the intended physical frame. The MoveIt tutorial says an initial camera-pose guess is not required for its workflow.
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Check the camera data before calibrating
Confirm that the camera image and sensor_msgs/CameraInfo are live and correspond to one another. The intrinsic camera parameters must be accurate, and the sensor coordinate frame must be correct. If the camera still needs intrinsic calibration, MoveIt points to the ROS camera_calibration package. A hand-eye solve cannot compensate for incorrect camera intrinsics or a mislabeled sensor frame.
Prepare a stationary, measurable target
The target must be flat for reliable camera localization, as MoveIt’s tutorial specifies. Place it on a flat surface or mount it on a board; keep it stationary relative to the robot base and visible from the sampled camera poses.
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MoveIt’s example target generator defaults to a 3-by-4 marker arrangement, 200-pixel marker size, 20-pixel separation, a one-bit marker border, and the DICT_5X5_250 ArUco dictionary. These are generator defaults, not universal dimensions or settings. You can generate and save the target image, then print it. The printed pattern must match the detector’s configured dictionary and geometry.
Measure the outside width of a marker and the separation between markers on the printed target, then enter those physical measurements in meters. Use the actual printed dimensions rather than assuming the software’s pixel settings determine the board’s physical size. A purchased flat board is optional; whichever target you use, its pattern, dictionary, measured dimensions, flatness, and visibility must suit the detector and calibration setup.
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Collect varied robot-and-camera pose pairs
Each calibration sample pairs two observations of the same target:
- The robot’s base-to-end-effector pose, obtained from robot kinematics.
- The camera-to-target pose, estimated from the image.
MoveIt’s tutorial makes calculation available after five samples and recommends collecting several more. Move the arm between observations and include rotations about at least two distinct axes; repeatedly rotating around only one axis does not provide the varied motion the described setup needs to solve the transform uniquely. Save joint states if you want to be able to repeat the poses during a later recalibration.
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The tutorial says improvement typically plateaus after about 12 or 15 samples. Treat that as workflow guidance, not a universal minimum, a guarantee of accuracy, or a substitute for checking the result. The number of useful samples depends on whether the observations provide varied, reliable geometry.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Solve the hand-eye transform and export it
MoveIt presents an AX=XB solver menu; Daniilidis is the documented default, which the tutorial describes as a good choice in most situations. After calculation, the camera pose is displayed and TF is updated. Saving the pose creates a launch file containing a static transform publisher. See the MoveIt tutorial for the Rolling documentation’s interface and workflow; Rolling documentation can change, and steps may differ across ROS releases, camera drivers, robot models, and calibration packages.
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Before using the exported transform for teleoperation, check that the publisher connects the intended parent and child frames, that its direction matches the physical relationship you calibrated, and that the values use the expected units. A transform published between the wrong frames—or in the wrong direction—can make a numerically successful solve unusable.
Validate against the robot and intended task
Check the transform on the actual robot and with the camera, target, and task you intend to use. Confirm that target observations and robot poses agree through the full TF chain, not merely that the solver produced a value. The MoveIt tutorial does not specify a numeric acceptance threshold, so set a tolerance based on the task’s requirements rather than treating the sample count or solver output as proof of accuracy.
MoveIt’s tutorial documents hand-eye calibration, not an end-to-end teleoperation performance guarantee. Latency, network behavior, controller response, safety limits, and robot-specific validation remain separate concerns.
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