Workshop 3 ‐ TFs and Sensors

Task 0: Launch the robot

Please refer to Workshop 1 if you don't remember what steps are required for starting your LIMO robot and all its sensors!

Task 1: Sensors (fairly quick, no more than 15 minutes on this!)

• Try the simulation environment (You will know how to start it, right?) or the real robot
• find Rviz
  • explore the different sensors (e.g., camera, LiDAR, IMU) in greater details. Find out what type of data they are and on which topics they publish.
  • find a way to visualise the tf tree (discuss with your student peers)
• make the robot move again and watch the output of the different sensors.

Task 2: TF tree and sensing

1. Display the tf tree of the LIMO robot (ros2 run rqt_tf_tree rqt_tf_tree), explain what a frame is (http://wiki.ros.org/tf might help, as might this scientific paper) ( If rqt_tf_tree is not installed. install with sudo apt update && sudo apt install ros-humble-rqt-tf-tree
2. find a way to display the position of the robot's camera (which frame does it have?) in global (/odom) coordinates
   • you may either implement Python code, following the TranformListener example or the given tf_listener.py, or
   • figure out how to use a command-line tool: ros2 run tf2_ros tf_echo
3. Based on the given tf_listener.py and on last week's mover.py, devise your own Python code. Create a publisher that publishes geometry_msgs/PoseStamped messages at the position of the closest laser scan reading and displays this pose in Rviz. Some useful pointers:
   • First, answer which frame the laser scans are in!
   • If you wonder how to get from ranges to coordinates, the community is never far away: https://answers.ros.org/question/304562/x-y-and-z-coordinates-in-laserscan/

Task 3: Tutorial to create a ROS workspace and package

• Basically, follow https://docs.ros.org/en/humble/Tutorials/Beginner-Client-Libraries/Creating-A-Workspace/Creating-A-Workspace.html
• e.g. create a directory cmp9767m_ws as the root of your workspace
• Optional (only if you feel confident enough in ROS, not essential):
  • Discuss the dependencies you may need and consider
  • Complete the package.xml with your own information,
• look at useful resources if you don't know git workflows
• decide on a name for your repository and create it to keep all your work in it (e.g, cmp9767_code) - you may want to follow the official instructions.
• add your own package(s) to the repository and keep track of all developments there. Only add your implementation to your source code repository (what is under src/ in your workspace)
  • you may want to include here the tflistener or the `mover script, or even the script you wrote as part of Workshop 2

Always Please make sure you keep this implementation safe (i.e. commit it to GitHub)

Sneak Peak (optional): Fun with OpenCV and ROS

THIS IS PURELY OPTIONAL. ONLY ENGAGE IF THE ABOVE ALL WORKS. IT'S TO GIVE YOU SOMETHING TO EXPLORE FURTHER.

Some coding practice in Python using cv_bridge

1. create a catkin package my_opencv_test, which should depend on cv_bridge and rospy (remember how to do that?)
2. be inspired by the implementation of opencv_test.py and code some small piece of python code that subscribes to the simulated cameras of your LIMO robot, and e.g. masks out any green stuff in the image
3. (optional) Also publish the result from the above operation