Many of the tf tutorials are available for both C++ and Python. The tutorials are streamlined to complete either the C++ track or the Python track. If you want to learn both C++ and Python, you should run through the tutorials once for C++ and once for Python. Note that the general concept itself is explained directly on tf package.
tf is deprecated in favor of tf2. tf2 provides a superset of the functionality of tf and is actually now the implementation under the hood. If you're just learning now it's strongly recommended to use the tf2/Tutorials instead.
Contents
Workspace Setup
If you have not yet created a workspace in which to complete the tutorials, click here for some brief instructions .
Learning tf
C++ |
Python |
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Now that you have completed these tutorials please take the time to complete this short questionnaire.
Debugging tf
Using sensor messages with tf
- Using Stamped datatypes with tf::MessageFilter
This tutorial describes how to use tf::MessageFIlter to process Stamped datatypes.
Setting up your robot with tf
- Setting up your robot using tf
This tutorial provides a guide to set up your robot to start using tf.
- Using the robot state publisher on your own robot
This tutorial explains how you can publish the state of your robot to tf, using the robot state publisher.
- Using urdf with robot_state_publisher
This tutorial gives a full example of a robot model with URDF that uses robot_state_publisher. First, we create the URDF model with all the necessary parts. Then we write a node which publishes the JointState and transforms. Finally, we run all the parts together.
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Video Demonstration
Watch the video below to have more explanation on transforms.