Only released in EOL distros:   fuerte

ROS Nodes

calibrate

Obtain the position and orientation of the kinect sensor by using landmarks on the field

Subscribed Topics

/camera/rgb/points (sensor_msgs/PointCloud2)

• The point cloud returned by the kinect containing an RGB channel along with the 3D point data.

/camera/rgb/image_color (sensor_msgs/Image)

• The image from the VGA camera on the kinect. It is used primarily for user input.

Placing the Kinect

1. Although the algorithm is independent of the sensor position, careful placement of a single sensor allows it to cover half the SPL field size (3m x 4m). I have had success placing it on the side of the half field at a height of about 2.5 - 3.5 meters.

2. Launch the calibrate node and adjust sensor appropriately until the field is visible as desired.

• roslaunch ground_truth calibrate.launch

Calibrating the Kinect

1. Launch the kinect driver

• roslaunch openni_camera openni_node.launch

2. Launch the calibrate node

• roslaunch ground_truth calibrate.launch

3. Follow the instructions on the bottom of the pointcloud visualizer screen. Remember that you have to click on the image and not the pointcloud!. A small sphere should appear in the point cloud visualizer window to indicate the point you clicked.

4. After you have selected all the ground plane points and the landmarks, the point cloud visualization should change so that the coordinate axes are aligned with the cloud. If you think the results look good you can terminate the program, as the calibration file has already been saved.

Command line arguments

1. The program supports a couple of command line arguments.

• -qsize <integer>         the ROS topic queue size (recommended value 1)
  -calibFile <string>      location where the calibration file should be stored
  -cam <string>            specification controlling the geometry of the 
                           visualizer window on the screen

2. The launch/calibrate.launch file can be used to specify these arguments, so that they do not have to be supplied every time. These arguments are the ones that are supplied every time you run the binary through the roslaunch command.

3. Alternatively, you can supply these parameters at command line directly

• rosrun ground_truth calibrate -qsize 1 -calibFile data/calib2.txt

detect

Find the locations of the robots and the orange ball on the field

Subscribed Topics

/camera/rgb/points (sensor_msgs/PointCloud2)

• The point cloud returned by the kinect containing an RGB channel along with the 3D point data.

Running the detect node

1. Launch the kinect driver

• roslaunch openni_camera openni_node.launch

2. Launch the detect node

• roslaunch ground_truth detect.launch

Command line arguments

1. The program supports a couple of command line arguments.

• -qsize <integer>         the ROS topic queue size (recommended value 1)
  -calibFile <string>      location of the calibration file
  -logFile <string>        location where the generated log file should be 
                           stored
  -colorTableFile <string> location of the color table lookup file
  -mode <integer>          mode = {1, 2}
  -cam <string>            specification controlling the geometry of the 
                           visualizer window on the screen

2. The launch/detect.launch file can be used to specify these arguments, so that they do not have to be supplied every time. These arguments are the ones that are supplied every time you run the binary through the roslaunch command.

3. Alternatively, you can supply these parameters at command line directly

• rosrun ground_truth detect -mode 2 -calibFile data/calib2.txt

logFile

Currently nothing is written to the log file. Check src/nodes/detect.cc for comments specifying where you could write to this log file.

mode

mode supports 2 values

• 1   FULL      Detection is not performed here. A skeletal of the field is 
                overlayed on to the cloud. Mainly used to see if the
                calibration process worked well 
  2   RELEVANT  This performs the detection. Detection is indicated by the appearance 
                of spheres on the visualization window.

Current Status

Sep/17/2011 NOTE(piyushk): The detect binary currently implements slightly different version of the heuristics for robot detection in comparison to those reported in the paper. This version is what we used for estimating parameters for our localization algorithm. I will try and revert back to the original algorithm soon