In this chapter, you will learn what is probably one of the most powerful features in ROS, something that will let you move our robot autonomously.

Thanks to the community and the shared code, ROS has many algorithms that can be used for navigation.

First of all, in this chapter, you will learn all the necessary ways to configure the navigation stack with your robot. In the next chapter, you will learn how to configure and launch the navigation stack on the simulated robot, giving goals and configuring some parameters to get the best results. In particular, we will cover the following topics in this chapter:

• Introduction to the navigation stacks and their powerful capabilities-clearly one of the greatest pieces of software that comes with ROS.
• The tf library-showing the transformation of one physical element to the other from the frame; for example, the data received using a sensor or the command for the desired position of an actuator. tf is a library for keeping track of the coordinate frames.
• Creating a laser driver or simulating it.
• Computing and publishing the odometry and how this is provided by Gazebo.
• Base controllers and creating one for your robot.
• Executing Simultaneous Localization and Mapping (SLAM) with ROS-building a map from the environment with your robot as it moves through it. Localizing your robot in the map using the Adaptive Monte Carlo Localization (AMCL) algorithm of the navigation stack. AMCL is a probabilistic localization system for a robot moving in 2D. It implements the AMCL approach, which uses a particle filter to track the pose of a robot against a known map.