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multiple_kinect_baxter_calibration

This repository contains code for calibration of Baxter robot and Kinect camera. It supports multiple Kinect sensors. It can be used (after minor modification) in other ROS supported robots as well.

Dependencies

  • Baxter SDK
    • Steps to install Baxter SDK can be found here
  • ar_track_alvar
    • Use following command to install ar_track_alvar package sudo apt-get install ros-indigo-ar-track-alvar
  • iai_kinect2
    • Tools for using the Kinect v2 in ROS
  • kinect_anywhere
    • If you want to use Kinect v2 on windows

Installation

  1. Download or clone the repository to the source directory of ROS workspace
  2. Compile the workspace by using catkin_make
  3. Mark the python scripts executable by using command below-
roscd multiple_kinect_baxter_calibration/scripts
chmod +x *.py

Steps for calibration

There are following three steps of the calibration process-

  1. Place the marker on Baxter arm
  2. Define a trajectory of Baxter arm
  3. Collect the data
  4. Compute the calibration
  5. Publish the calibration

Below are the details of each step.

Place the marker on Baxter arm

We are using a green colored sphere as a marker as shown below- setup

Define a trajectory of Baxter arm

Each Baxter Kinect setup varies due to the the location of camera. Hence, prior to data collection step, we must need to define a trajectory of Baxter arm by following steps mentioned below-

  1. Record way-points of the arm trajectory by executing the following script.
rosrun multiple_kinect_baxter_calibration trajectory_waypoints_recorder.py _file:=baxter.csv _limb:=right

Following are the valid parameters for this script-

  • _file:= [type: string] filename to store all the way-points as csv.
    • Default value: No value
  • _limb:= [type: string] name of the baxter arm, in which the marker is attached. The value of this parameter can only be left or right
    • Default value: right
  1. Make sure to enable the Zero-G mode so that the arm can be moved easily to any location by grasping the cuff over its groove.
  2. Press the Baxter button on the arm in order to record the way-point.
  3. Record 10 (higher is better) different way-points. Press CTRL+C to stop the recording process.

Collect the data

  1. Start the kinect by using following command-
  • For iai_kinect2: roslaunch kinect2_bridge kinect2_bridge.launch
    • Following are the valid parameters for this script. Check here.
  • For kinect_anywhere: roslaunch kinect_anywhere kinect_anywhere.launch pointcloud:=true kinect_frame:=kinect2_link
    • Following are the valid parameters for this script. Check here.
  1. Start collecting the data by using following command-
roslaunch multiple_kinect_baxter_calibration calibration_data_collector.launch topic:=/kinect2/sd/points kinect2_trajectory:=/home/ravi/ros_ws/src/multiple_kinect_baxter_calibration/files/baxter.csv

Following are the valid parameters for this script-

  • topic:= [type: string] rostopic for receving point cloud
    • Default value: No value
  • limb:= [type: string] limb used in calibration process
    • Default value: right
  • log:= [type: string] log level parameter. It must be one of the following- Info, Debug, Warn, Error, Fatal.
    • Default value: Info
  • kinect1_trajectory:= [type: string] full path to the baxter arm pre-defined trajectory for kinect1
    • Default value: /home/ravi/ros_ws/src/multiple_kinect_baxter_calibration/files/viapoints.csv
  • kinect2_trajectory:= [type: string] full path to the baxter arm pre-defined trajectory for kinect2
    • Default value: /home/ravi/ros_ws/src/multiple_kinect_baxter_calibration/files/viapoints.csv
  • kinect3_trajectory:= [type: string] full path to the baxter arm pre-defined trajectory for kinect3
    • Default value: /home/ravi/ros_ws/src/multiple_kinect_baxter_calibration/files/viapoints.csv
  • kinect_anywhere_trajectory:= [type: string] full path to the baxter arm pre-defined trajectory for kinect anywhere
    • Default value: /home/ravi/ros_ws/src/multiple_kinect_baxter_calibration/files/viapoints.csv
  • min_hsv:= [type: string] minimum HSV value for sphere segmentation as min_hsv:="[40, 50, 60]"
    • Default value: "[40, 50, 60]"
  • max_hsv:= [type: string] maximum HSV value for sphere segmentation as max_hsv:="[60, 200, 255]"
    • Default value: "[60, 200, 255]"
  • radius:= [type: float] radius of sphere (in meter)
    • Default value: 0.05
  • offset:= [type: float] length of stick to hold the sphere (in meter)
    • Default value: 0.0343
  • k_neighbors:= [type: int] number of 'k' nearest neighbors to use for feature estimation
    • Default value: 10
  • weight:= [type: double] normal angular distance weight
    • Default value: 0.05
  • max_itr:= [type: int] maximum number of iterations before giving up
    • Default value: 1000
  • d_thresh:= [type: double] distance to the model threshold
    • Default value: 0.005
  • prob:= [type: double] probability of choosing at least one sample free from outliers
    • Default value: 0.99999
  • tolerance:= [type: double] tolerance in radius (in meters)
    • Default value: 0.01
  • epsilon:= [type: double] angle epsilon (delta) threshold (in degree)
    • Default value: 15
  • data_dir:= [type: string] directory for saving tracking data
    • Default value: /home/ravi/ros_ws/src/multiple_kinect_baxter_calibration/files
  • queue_size:= [type: int] queue_size for the subscribers
    • Default value: 1
  • wait_time:= [type: double] wait time to stablize arm before capturing point cloud (in seconds)
    • Default value: 2
  • max_samples:= [type: int] maximum number of samples at any waypoint
    • Default value: 5
  • min_z:= [type: float] minimum z coordinate value of point cloud w.r.t. camera
    • Default value: 0.5
  • max_z:= [type: float] maximum z coordinate value of point cloud w.r.t. camera
    • Default value: 5.0
  • title_bar_height:= [type: int] height of the title bar in point cloud visualizer window (in pixel)
    • Default value: 10

Compute the calibration

roslaunch multiple_kinect_baxter_calibration calibration_compute.launch kinect:=kinect2

Following are the valid parameters for this script-

  • data_dir:= [type: string] directory of baxter_trajectory and kinect_trajectory file
    • Default value: /home/ravi/ros_ws/src/multiple_kinect_baxter_calibration/files/
  • kinect:= [type: string] name/id of the kinect as kinect:=kinect1
    • Default value: No value

Publish the calibration

roslaunch multiple_kinect_baxter_calibration calibration_publisher.launch calibration:="[kinect2]"

Following are the valid parameters for this script-

  • data_dir:= [type: string] directory of baxter_trajectory and kinect_trajectory file
    • Default value: /home/ravi/ros_ws/src/multiple_kinect_baxter_calibration/files/
  • calibration:= [type: string] all the names/ids of the kinects as calibration:="[kinect1, kinect2, kinect3]"
    • Default value: "[kinect1, kinect2, kinect3]"

Other utility files

view_cloud_realtime

To view the point cloud data in real-time

rosrun multiple_kinect_baxter_calibration view_cloud_realtime _topic:="/kinect2/sd/points"

Following are the valid parameters for this script-

  • _topic:= [type: string] rostopic for subscribing to point cloud
    • Default value: "/kinect1/sd/points"
  • _source:= [type: string] source of the point cloud. It can be Windows or Linux
    • Default value: Linux

save_pcd

To save the point cloud data in a PCD file

rosrun multiple_kinect_baxter_calibration save_pcd _topic:="/kinect2/sd/points"

Following are the valid parameters for this script-

  • _topic:= [type: string] rostopic for subscribing to point cloud
    • Default value: "/kinect1/sd/points"

view_pcd

To visualize the stored point cloud file

rosrun multiple_kinect_baxter_calibration view_pcd _file:=scene.pcd

Following are the valid parameters for this script-

  • _file:= [type: string] full path of the point cloud file
    • Default value: "/home/ravi/ros_ws/src/multiple_kinect_baxter_calibration/files/scene.pcd"
  • _source:= [type: string] source of the point cloud. It can be Windows or Linux
    • Default value: Linux

Please press j to take screenshot of the current scene.

segment_image

To find out the HSV range of the colored marker in the image

rosrun multiple_kinect_baxter_calibration segment_image _file:=scene.jpg

Following are the valid parameters for this script-

  • _file:= [type: string] full path of the image file
    • Default value: No value

view_image

To find out the RGB and HSV value of any pixel in the image

rosrun multiple_kinect_baxter_calibration view_image _file:=scene.jpg

Following are the valid parameters for this script-

  • _file:= [type: string] full path of the image file
    • Default value: No value

sphere_detector_test

To test whether sphere segmentation is working or not

rosrun multiple_kinect_baxter_calibration sphere_detector_test _file:=scene.pcd

Following are the valid parameters for this script-

  • _file:= [type: string] full path of the point cloud file
    • Default value: "/home/ravi/ros_ws/src/multiple_kinect_baxter_calibration/files/scene.pcd"
  • _r:= [type: float] radius of sphere (in meter)
    • Default value: 0.05
  • _min_h:= [type: int] minimum hue for sphere segmentation
    • Default value: 40
  • _min_s:= [type: int] minimum saturation for sphere segmentation
    • Default value: 50
  • _min_v:= [type: int] minimum value for sphere segmentation
    • Default value: 60
  • _max_h:= [type: int] maximum hue for sphere segmentation
    • Default value: 60
  • _max_s:= [type: int] maximum saturation for sphere segmentation
    • Default value: 200
  • _max_v:= [type: int] maximum value for sphere segmentation
    • Default value: 255
  • _source:= [type: string] source of the point cloud. It can be Windows or Linux
    • Default value: Linux

For three Kinects running on iai_kinect2

  1. Initialize project and start all the kinects
roslaunch multiple_kinect_baxter_calibration init.launch
  1. Collect the data for kinect1
roslaunch multiple_kinect_baxter_calibration calibration_data_collector.launch topic:=/kinect1/sd/points
  1. Compute the calibration for kinect1
roslaunch multiple_kinect_baxter_calibration calibration_compute.launch kinect:=kinect1
  1. Repeate steps 2 and 3 for kinect2 and kinect3
  2. Publish the calibration data
roslaunch multiple_kinect_baxter_calibration calibration_publisher.launch calibration:="[kinect1, kinect2, kinect3]"

For kinect_anywhere

roslaunch kinect_anywhere kinect_anywhere.launch color:=false body:=true pointcloud:=true kinect_frame:=kinect1_link
roslaunch multiple_kinect_baxter_calibration calibration_data_collector.launch topic:=/kinect_anywhere/point_cloud/points2
roslaunch multiple_kinect_baxter_calibration calibration_compute.launch kinect:=kinect_anywhere

Open calibration file and modify child to kinect1_link

roslaunch multiple_kinect_baxter_calibration calibration_publisher.launch calibration:="[kinect_anywhere]"

Issues (or Error Reporting)

Please check here and create issues accordingly.

multiple_kinect_baxter_calibration's People

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multiple_kinect_baxter_calibration's Issues

Why use a green ball as maker? How can I use AR code for calibration?

Thanks for your wonderful work! But I have a question for this calibration code : Why do you use a green ball as maker which I can not obtain. Can you tell me how I can use AR code for calibration which I can easily obtaion? I am newer for robotic field. Thanks for your kindly reply!

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