Skip to content

Latest commit

 

History

History
106 lines (79 loc) · 5.88 KB

README.md

File metadata and controls

106 lines (79 loc) · 5.88 KB
Raw

handeye-4dof: Handeye Calibration for 4DOF Manipulators


Figure 1. Handeye calibration for 4DOF SCARA arms [1]. Left is eye-on-base and right is eye-on-hand.

This package provides handeye calibration for 4DOF robot manipulators using dual quaternions. This is an implementation of [1] which extended dual quaternion handeye calibration techniques [2] to 4DOF robots. Handeye calibration through dual quaternions is favorable as it allows for us to solve for translation and rotation simultaneously, thus avoiding any compounding error resultant from solving these separately.

Background

Robot handeye calibration is an important and well studied problem that allows us to obtain the unknown static transformation between the end effector and camera (in eye-on-hand case) or the robot base and camera (in eye-on-base case). This is done by sampling several transformations between the base and end effector (through forward kinematics) and camera to marker (through marker detection methods) which allows us to formulate the AX = XB problem where X is our unknown desired static transform.

Several methods exist to solve for X but a vast majority of these methods assume that the robot is well articulated (i.e. has 6DOF). For 4DOF robots such as SCARAs (x, y, z, yaw), conventional calibration methods are infeasible and are incapable of producing valid results. This method circumvents this problem by reducing [2] to 4DOF robots. See [1] for further details. For an introduction to dual quaternions, see [3].

Dependencies

First, install the necessary packages.

python3 -m pip install -r requirements.txt

How to Run

There are two possible types of calibrations you can perform:

  1. Eye-in-hand: where the transform of interest is from hand to camera or
  2. Eye-on-base: where the transform of interest is from camera to base.

The necessary sampled transforms for calibration thus depend on the type of calibration chosen. Below, we can see the simple transform chaining used for both types of calibration.

Eye-in-hand calibration

camera_T_marker * hand_T_camera * base_T_hand = base_T_marker
   (known)       (unknown, want)     (known)   (unknown, don't care)

For this type of calibration we require a set of (camera to marker) and (base to hand) transforms, which will then be used to obtain the desired (hand to camera) transform.

Eye-on-base calibration

base_T_hand * camera_T_base * marker_T_camera = marker_T_hand
   (known)   (unknown, want)     (known)   (unknown, don't care)

For this type of calibration we require a set of (base to hand) and (marker to camera) transforms, which will then be used to obtain the desired (camera to base) transform.

Setting up the calibration

The expected format for poses is a pickle file containing a tuple consisting of a list of the first desired transform and a list of the second desired transforms (in the order specified above). All transforms should be 4x4 numpy arrays. Afterwards, calibration can be performed by running the command shown below.

cd src # For descriptions on the command line arguments, run python3 calibrate.py -h
python3 calibrate.py [-h] [-p POSE_PATH] [-m MOTION_PATH] [-c] [-a] [-n] [-s SAMPLE]

For those who wish to create their own custom scripts, an example template is provided in src/example.py (small snippet shown below). Running these examples will also perform the calibration on the provided example data.

""" ========================= Eye-in-Hand Calibration Example =============================== """
def main():
    with open("../example_data/pose_samples.pkl", "rb") as f:
        try:
            base_to_hand, camera_to_marker = pickle.load(f)
        except UnicodeDecodeError:
            # python 2 to python 3 pickle in case sampling was done in ROS
            base_to_hand, camera_to_marker = pickle.load(f, encoding='latin1')

    # Obtain optimal motions as dual quaternions.
    motions = robot_pose_selector(camera_to_marker, base_to_hand)

    # Initialize calibrator with precomputed motions.
    cb = Calibrator4DOF(motions)

    # Our camera and end effector z-axes are antiparallel so we apply a 180deg x-axis rotation.
    dq_x = cb.calibrate(antiparallel_screw_axes=True)

    # Hand to Camera TF obtained from handeye calibration.
    ca_hand_to_camera = np.linalg.inv(dq_x.as_transform())

    # Hand to Camera TF obtained from post nonlinear refinement.
    nl_hand_to_camera = cb.nonlinear_refinement(base_to_hand, camera_to_marker, ca_hand_to_camera)

Some example data for eye-on-base can be seen provided in issue #2.

Note: that the nonlinear refinement step has only been validated to work for eye-in-hand. For users performing eye-on-base, it is recommended to skip this step for now.

TODO

If you would like to contribute by implementing any of the tasks below or you spot an error in the existing code base, feel free to submit a pull request!

  • Add eye-on-base functionality
  • Improve README documentation.
  • Add ROS capabilities.
  • Add graphical visualizations / GUI.
  • Add command line interface.
  • Add post nonlinear refinement.
  • Add singular value checking.
  • Add pose selection based off of dual quaternion scalar score (tried but not working).
  • Add details for z-translation obtainment. (Some details in the discussion of issue #2)
  • Test and validate the nonlinear refinement for eye-on-base.

References

[1] Ulrich, Markus. “Hand-Eye Calibration of SCARA Robots.” (2014).

[2] Daniilidis, Kostas. “Hand-Eye Calibration Using Dual Quaternions.” The International Journal of Robotics Research 18 (1999): 286 - 298.

Dual Quaternion Guide

[3] Kenwright, Ben. “A Beginners Guide to Dual-Quaternions: What They Are, How They Work, and How to Use Them for 3D Character Hierarchies.” WSCG 2012 (2012).