[Documentation] [TitleIndex] [WordIndex] 

现在有许多 tf 的教程可用于 C++ 和 Python。这些教程大多是流程化的，可以用 C++ 或者 Python 来完成。如果您既想学 C++ 又想学 Python，您应该用 C++ 和 Python 各跑一遍教程。请注意，大致的概念已经在tf 功能包页面中解释。

Contents

  1. Workspace Setup
  2. 学习 tf
  3. 调试 tf
  4. 用 tf 处理传感器消息
  5. 用 tf 设置机器人
  6. 视频教程 (PR2 Beta Workshop)

Workspace Setup

If you have not yet created a workspace in which to complete the tutorials, click here for some brief instructions .

  Show EOL distros: 

Create a file named ~/tutorials.rosinstall with the following content: 

- other: { local-name: workspace }

To overlay on the ROS distro you are using: 

rosinstall ~/tutorials /opt/ros/$ROS_DISTRO>> ~/tutorials.rosinstall

To use this workspace whenever you open a new terminal setup your ROS environment by typing: 

source ~/tutorials/setup.bash

Sourcing this file adds ~/tutorials/workspace to your ROS_PACKAGE_PATH.

Any packages you create in that directory will be found by rospack.

An alternative to source your script file is to add it to your .bashrc, but remember that this will persist in your .bashrc into the future, and you can only have one environment setup. For more on what this is doing see this page

Create a catkin workspace like so: 

$ source /opt/ros/$ROS_DISTRO/setup.bash
$ mkdir -p ~/tutorial_ws/src
$ cd ~/tutorial_ws
$ catkin_init_workspace src
$ catkin_make

And now source the setup file from the result-space, so that packages you add to this workspace's src folder will be findable by rospack, and the built binaries by rosrun and roslaunch: 

$ source devel/setup.bash

学习 tf

C++

Python

  1. Writing a tf broadcaster (C++)

    This tutorial teaches you how to broadcast coordinate frames of a robot to tf.

  2. Writing a tf listener (C++)

    This tutorial teaches you how to use tf to get access to frame transformations.

  3. Adding a frame (C++)

    This tutorial teaches you how to add an extra fixed frame to tf.

  4. Learning about tf and time (C++)

    This tutorial teaches you to use the waitForTransform function to wait for a transform to be available on the tf tree.

  5. Time travel with tf (C++)

    This tutorial teaches you about advanced time travel features of tf

  1. Writing a tf broadcaster (Python)

    This tutorial teaches you how to broadcast the state of a robot to tf.

  2. Writing a tf listener (Python)

    This tutorial teaches you how to use tf to get access to frame transformations.

  3. Adding a frame (Python)

    This tutorial teaches you how to add an extra fixed frame to tf.

  4. Learning about tf and time (Python)

    This tutorial teaches you to use the waitForTransform function to wait for a transform to be available on the tf tree.

  5. Time travel with tf (Python)

    This tutorial teaches you about advanced time travel features of tf

如果您已经完成这些教程，请协助我们完成这个简短的 问卷。

调试 tf

  1. tfの問題をデバッグする

    このチュートリアルでは、tf関連のデバッグの体系的なアプローチについて学びます

  2. Debugging tf problems

    This tutorial gives a systematic approach for debugging tf related problems.

用 tf 处理传感器消息

  1. No Title

    No Description

用 tf 设置机器人

  1. Setting up your robot using tf

    This tutorial provides a guide to set up your robot to start using tf.

  2. 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.

  3. 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.

  1. 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.

  2. 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.

创建一个新教程:

视频教程 (PR2 Beta Workshop)

2024-11-16 14:32