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Learning ROS for Robotics Programming Second Edition

Learning ROS for Robotics Programming Second Edition

By : Mahtani, Aaron Martinez
4 (8)
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Learning ROS for Robotics Programming Second Edition

Learning ROS for Robotics Programming Second Edition

4 (8)
By: Mahtani, Aaron Martinez

Overview of this book

If you have ever tried building a robot, then you know how cumbersome programming everything from scratch can be. This is where ROS comes into the picture. It is a collection of tools, libraries, and conventions that simplifies the robot building process. What’s more, ROS encourages collaborative robotics software development, allowing you to connect with experts in various fields to collaborate and build upon each other's work. Packed full of examples, this book will help you understand the ROS framework to help you build your own robot applications in a simulated environment and share your knowledge with the large community supporting ROS. Starting at an introductory level, this book is a comprehensive guide to the fascinating world of robotics, covering sensor integration, modeling, simulation, computer vision, navigation algorithms, and more. You will then go on to explore concepts like topics, messages, and nodes. Next, you will learn how to make your robot see with HD cameras, or navigate obstacles with range sensors. Furthermore, thanks to the contributions of the vast ROS community, your robot will be able to navigate autonomously, and even recognize and interact with you in a matter of minutes. What’s new in this updated edition? First and foremost, we are going to work with ROS Hydro this time around. You will learn how to create, visualize, and process Point Cloud information from different sensors. This edition will also show you how to control and plan motion of robotic arms with multiple joints using MoveIt! By the end of this book, you will have all the background you need to build your own robot and get started with ROS.
Table of Contents (12 chapters)
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11
Index

Calibrating the camera


Most cameras, especially wide-angle ones, exhibit large distortions. We can model such distortions as radial or tangential and compute the coefficients of that model using calibration algorithms. The camera calibration algorithms also obtain a calibration matrix that contains the focal distance and principle point of the lens and, hence, provide a way to measure distances in the world using the images acquired. In the case of stereo vision, it is also possible to retrieve depth information, that is, the distance of the pixels to the camera, as we will see later. Consequently, we have 3D information of the world up to an extent.

The calibration is done by showing several views of a known image named calibration pattern, which is typically a chessboard/checkerboard. It can also be an array of circles or an asymmetric pattern of circles; note that circles are seen as ellipses by the camera for skew views. A detection algorithm obtains the inner corner point of the cells...

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