Book Image

Mastering ROS for Robotics Programming - Second Edition

By : Jonathan Cacace, Lentin Joseph
Book Image

Mastering ROS for Robotics Programming - Second Edition

By: Jonathan Cacace, Lentin Joseph

Overview of this book

In this day and age, robotics has been gaining a lot of traction in various industries where consistency and perfection matter. Automation is achieved via robotic applications and various platforms that support robotics. The Robot Operating System (ROS) is a modular software platform to develop generic robotic applications. This book focuses on the most stable release of ROS (Kinetic Kame), discusses advanced concepts, and effectively teaches you programming using ROS. We begin with aninformative overview of the ROS framework, which will give you a clear idea of how ROS works. During the course of this book, you’ll learn to build models of complex robots, and simulate and interface the robot using the ROS MoveIt! motion planning library and ROS navigation stacks. Learn to leverage several ROS packages to embrace your robot models. After covering robot manipulation and navigation, you’ll get to grips with the interfacing I/O boards, sensors, and actuators of ROS. Vision sensors are a key component of robots, and an entire chapter is dedicated to the vision sensor and image elaboration, its interface in ROS and programming. You’ll also understand the hardware interface and simulation of complex robots to ROS and ROS Industrial. At the end of this book, you’ll discover the best practices to follow when programming using ROS.
Table of Contents (22 chapters)
Title Page
Copyright and Credits
www.PacktPub.com
Contributors
Preface
Index

Working with AR Marker detection for object pose estimation


In this section, we will see how to use fiducial markers in order to enable a robot to easily interact with its environment. To interact with arbitrary objects, a robot should be able to recognize and localize them by relying on its vision sensors. Estimating the pose of an object represents an important feature of all robotic and computer-vision applications. However, efficient algorithms to perform object recognition and pose estimation working in real-world environments are difficult to implement, and in many cases one camera is not enough to retrieve the three-dimensional pose of an object.

More precisely, with the use of only one fixed camera, it is not possible to get spatial information about the depth of a framed scene. For this reason, object pose estimation is often simplified by exploiting AR markers. An AR marker is typically represented by a synthetic square image composed by a wide black border and an inner binary matrix...