Robotic Hand-eye Calibration Workspace

| Ubuntu 1804 & ROS Melodic Morenia |

This repo contains a eye-in-hand calibration tool (Cplusplus & ROS) in JD京东 GRASPING ROBOT CHALLENGE (News),
and the implements of my paper: Robotic hand-eye calibration with depth camera: A sphere model approach (PDF)

Inside /src there are 5 ROS packages:

rgbd_srv
used by camera_driver.
camera_driver
drive Intel® RealSense™ RGBD cameras in ROS.
(convert raw stream to ros sensor_msgs::Image)
camera_transform_publisher
publish the transformation matrix (extrinsics) between camera and a marker( chessboard | aruco tag).
handeye_calib_marker
handeye calib tools that use RGB camera and a marker, modified from handeye_calib_camodocal
----------------------------------------------
with the package above you can calibrate the eye-in-hand transformation on RGB images.
handeye_calib_sphere
fine-tune the eye-in-hand result based on depth.

Prerequisit

Ubunutu 18.04
ROS Melodic Morenia (desktop-full install)
with OpenCV & Opencv_contrib install in /usr/local (both 3.4.0)
librealsense if you use Intel® RealSense™ RGBD cameras (D400 series and the SR300)
visp_hand2eye_calibration_calibrator
$ sudo apt-get install ros-melodic-visp-hand2eye-calibration
glog at here (0.4.0)
Ceres-solver download at here (1.14.0) and instruction at here
Sophus at here (1.0.0)
ros-melodic-cv-bridge *
$ sudo apt-get install ros-melodic-cv-bridge
Point Cloud Library (PCL)
$ sudo apt-get install libpcl-dev ros-melodic-pcl-ros

Install opencv & opencv_contrib

Download opencv and opencv_contrib (both tested on 3.4.0).
Install opencv prerequisit:

[compiler] $ sudo apt-get install build-essential
[required] $ sudo apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev
[optional] $ sudo apt-get install python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev

Build opencv & opencv_contrib from source: (we turned off the cuda options)

$ mkdir build && cd build 
$ cmake \
    -D CMAKE_BUILD_TYPE=Release \
    -D OPENCV_EXTRA_MODULES_PATH= <path/to/opencv_contrib-3.4.0>/modules/ \
    -D BUILD_opencv_cudacodec=OFF \
    -D WITH_CUDA=OFF \
    -D WITH_CUBLAS=OFF \
    -D WITH_CUFFT=OFF \
    -D ENABLE_PRECOMPILED_HEADERS=OFF \
    -D CMAKE_INSTALL_PREFIX=/usr/local ..

$ make -j5
$ sudo make install

Install librealsene (if you use Intel® RealSense™ RGBD Camera)

We use SR300/D415 camera and use camera_driver package to convert raw RGBD images into ROS topic of sensor_msgs::Image. The librealsense is required only if you use cameras from RealSense™ family, otherwise make your own camera_driver.

Following the librealsense installation guide at here.
We installed: librealsense2-dkms, librealsense2-utils, librealsense2-dev, librealsense2-dbg

Install cv_bridge from source.

If you are lucky enough, the ros pre-build ros-melodic-cv-bridge works well.
However, since the default OpenCV in ros-melodic-cv-bridge is 3.2, while we use opencv & opencv_contrib in 3.4.0, we recommend you to build the cv_bridge from source.

$ git clone https://github.com/ros-perception/vision_opencv.git
$ cd vision_opencv
$ git checkout melodic
$ cd cv_bridge

$ mkdir build && cd build
$ cmake ..
$ make -j5
$ sudo make install

In the meantime, remove the pre-bulid cv_bridge in ros-melodic path /opt/ros/melodic:

$ cd /opt/ros/melodic

# or you can archive them to other place
$ sudo rm -rf lib/libcv_bridge.so
$ sudo rm -rf include/cv_bridge
$ sudo rm -rf share/cv_bridge

For more information about cv_bridge, see here.

Build

$ git clone https://github.com/lixiny/Handeye-Calibration-ROS.git
$ cd Handeye-Calibration-ROS  
$ catkin_make

if you only want to calibrate hand-eye transformation on RGB marker,

$ git clone https://github.com/lixiny/Handeye-Calibration-ROS.git  
$ cd Handeye-Calibration-ROS  
$ git checkout rgb_only
$ catkin_make

Ready to Use (Example on RealSense D415 & UR5)

0. Robot and Camera Setup

Prepare a camera support that mount camera on the robot. We provide the support files [.STL] for UR5 and RealSense D415 & SR300.

Navigate to the Handeye-Calibration-ROS/ folder, and source devel/setup.bash in each new terminal.

1. Pluged in Camera, Bringup RealSense

$1 roslaunch camera_driver realsense_driver.launch

The camera intrinsic (later we call it intr) will appear in current terminal, record it.

The camera_driver publishes 3 topics to rosmaster:

/realsense/rgb
/realsense/depth
/realsense/cloud
the point cloud is under tf frame: /camera_link
/realsense/camera_info
the camera_info followed ros sensor_msgs/CameraInfo.msg, we only use K and D in it.

You can also visualize these topics in rviz.

2. Prepare a Marker

You can either use a chessboard or an aruco plane at doc/rawArucoPlane.jpg we designed.

3. Publish the Transformation (tf) Between Camera and Marker

😄 Chessboard

After you placed the chessboard inside camera view, run:

$2 roslaunch camera_transform_publisher chessboard_publisher_realsense.launch

# or specify chessboard parameters:
$2 roslaunch camera_transform_publisher chessboard_publisher_realsense.launch \
        chessboardWidth:=10  chessboardHeight:=7  squareSize:=0.02

[IMPORTANT] There are 5 user-specified parameters in chessboard_publisher_realsense.launch:

chessboardWidth -- # of inner corners on chessboard width direction
chessboardHeight -- # of inner corners on chessboard height direction
squareSize -- length of side of each square on chessboard, in meter.
cameraTopic -- the rgb topic name published in your camera_driver, in ours: /realsense/rgb
cameraInfoTopic -- the camera_info topic name published in your camera_driver, (/realsense/camera_info)

Make sure you have already checked them based on your chessboard.

😉 Aruco Plane

After you placed our aruco plane inside camera view, run:

$2 roslaunch camera_transform_publisher aruco_publisher_realsense.launch

# or specify aruco plane parameters:
$2 roslaunch camera_transform_publisher aruco_publisher_realsense.launch \
        tagSideLen:=0.035 planeSideLen:=0.25

[IMPORTANT] There are 4 user-specified parameters in aruco_publisher_realsense.launch:

tagSideLen -- side length of a single aruco tag, in meter. (see illustration)
planeSideLen -- side length of the whole aruco plane, in meter.(see illustration)
cameraTopic -- the rgb topic name published in your camera_driver, in ours: /realsense/rgb
cameraInfoTopic -- the camera_info topic name published in your camera_driver, (/realsense/camera_info)

Make sure you have already checked them based on the physical length of your aruco plane.

Now in the pop-up window, you will see an AR cube like this:

This camera_transform_publisher will publish a tf (/camera_link, /ar_marker) to ros master. If no marker found, a Identity SE3 tf will be used. You can also visualize the two frame in rviz.

4. Bringup UR

Followed the instrction #3. Bringup UR in doc/install_ur.md.

5. Launch Hand-eye Calibration on Marker

$3 roslaunch handeye_calib_marker handeye_online.launch

[IMPORTANT] There are 4 user-specified parameters in handeye_online.launch:

ARTagTF -- the name of marker frame (defined in camera_transform_publisher, /ar_marker)
cameraTF -- the name of camera frame (defined in camera_transform_publisher, /camera_link)
EETF -- the name of End Effector frame (defined by UR, /ee_link)
baseTF -- the name of robot base frame (defined by UR, /base_link)

Check these 4 tf names before you launch calibraion!

start calibration

Repeatedly move UR end effector to different configuration. Meanwhile, make sure at each unique configuration, a valid marker can be detected. In current terminal, press s to record one AX=XB equation. After sufficient # of equations have been recorded (30+), press q to perform calibraition. Then in current terminal, you will see some output like:

publish the results

modify the file: handeye_calib_marker/launch/show_result.launch, replace the ${Translation: x y z} and ${Rotation: qx qy qz qw} based on your terminal output.

<launch>
  <node pkg="tf" 
        type="static_transform_publisher" 
        name="realsense_link" 
        args="${Translation: x y z}
              ${Rotation: qx qy qz qw}
              /ee_link 
              /camera_link 
              100"
  />
</launch>

and launch:

$4 roslaunch handeye_calib_marker show_result.launch

or, in a new terminal:

# rosrun tf static_transform_publisher \
#           x y z qx qy qz qw \
#           frame_id  child_frame_id  period_in_ms

$4 rosrun tf static_transform_publisher ＼
        0.0263365 0.0389317 0.0792014 -0.495516 0.507599 -0.496076 0.500715 \
        /ee_link   /camera_link   100

visualize in rviz

There are several representations/expressions of the HAND-EYE problem:

HAND-EYE is the transformation from /ee_link to /camera_link
or tf: (/ee_link, /camera_link)
or father: /ee_link, child: /camera_link
or: P_{ee_link} = T_handeye * P_{camera_link}

NOTE: now you should have a ready-to-use handeye transformation.
This results is optimized only based on RGB images. You can still fine-tune the result with depth image (if has) following setp # 6.

6. (Optional) Fine-tune on Sphere

// TODO

Related Publications:

Yang, Lixin, et al. " Robotic hand-eye calibration with depth camera: A sphere model approach. " 2018 4th International Conference on Control, Automation and Robotics (ICCAR). IEEE, 2018. PDF

@inproceedings{yang2018robotic,
  title={Robotic hand-eye calibration with depth camera: A sphere model approach},
  author={Yang, Lixin and Cao, Qixin and Lin, Minjie and Zhang, Haoruo and Ma, Zhuoming},
  booktitle={2018 4th International Conference on Control, Automation and Robotics (ICCAR)},
  pages={104--110},
  year={2018},
  organization={IEEE}
}

License

This repo is freely available for free non-commercial use, and may be redistributed under these conditions. Please, see the license for further details

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