Book Image

ROS Robotics Projects

Book Image

ROS Robotics Projects

Overview of this book

Robot Operating System is one of the most widely used software frameworks for robotic research and for companies to model, simulate, and prototype robots. Applying your knowledge of ROS to actual robotics is much more difficult than people realize, but this title will give you what you need to create your own robotics in no time! This book is packed with over 14 ROS robotics projects that can be prototyped without requiring a lot of hardware. The book starts with an introduction of ROS and its installation procedure. After discussing the basics, you’ll be taken through great projects, such as building a self-driving car, an autonomous mobile robot, and image recognition using deep learning and ROS. You can find ROS robotics applications for beginner, intermediate, and expert levels inside! This book will be the perfect companion for a robotics enthusiast who really wants to do something big in the field.
Table of Contents (20 chapters)
ROS Robotics Projects
Credits
About the Author
Acknowledgements
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Working with the face-tracking ROS package


We have already created or copied the face_tracker_pkg package to the workspace and have discussed some of its important dependencies. Now, we are going to discuss what this package exactly does!

This package consists of a ROS node called face_tracker_node that can track faces using OpenCV APIs and publish the centroid of the face to a topic. Here is the block diagram of the working of face_tracker_node:

Figure 15: Block diagram of face_tracker_node

Let's discuss the things connected to face_tracker_node. One of the sections that may be unfamiliar to you is the face Haar classifier:

  • Face Haar classifier: The Haar feature-based cascade classifier is a machine learning approach for detecting objects. This method was proposed by Paul Viola and Michael Jones in their paper Rapid Object detection using a boosted cascade of simple features in 2001. In this method, a cascade file is trained using a positive and negative sample image, and after training...