  Autonomous navigation is a long-standing field of robotics research, which
provides an essential capability for mobile robots to execute a series of tasks
on the same environments performed by human everyday. In this chapter, we
present a set of algorithms to train and deploy deep networks for autonomous
navigation of mobile robots using the Robot Operation System (ROS). We describe
three main steps to tackle this problem: i) collecting data in simulation
environments using ROS and Gazebo; ii) designing deep network for autonomous
navigation, and iii) deploying the learned policy on mobile robots in both
simulation and real-world. Theoretically, we present deep learning
architectures for robust navigation in normal environments (e.g., man-made
houses, roads) and complex environments (e.g., collapsed cities, or natural
caves). We further show that the use of visual modalities such as RGB, Lidar,
and point cloud is essential to improve the autonomy of mobile robots. Our
project website and demonstration video can be found at
https://sites.google.com/site/autonomousnavigationros.
