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  • Book Overview & Buying Learning ROS for Robotics Programming Second Edition
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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

Adaptive Monte Carlo Localization


In this chapter, we are using the amcl (Adaptive Monte Carlo Localization) algorithm for the localization. The amcl algorithm is a probabilistic localization system for a robot moving in 2D. This system implements the adaptive Monte Carlo localization approach, which uses a particle filter to track the pose of a robot against a known map.

The amcl algorithm has many configuration options that will affect the performance of localization. For more information on amcl, please refer to the AMCL documentation at http://wiki.ros.org/amcl and also at http://www.probabilistic-robotics.org/.

The amcl node works mainly with laser scans and laser maps, but it could be extended to work with other sensor data, such as a sonar or stereo vision. So for this chapter, it takes a laser-based map and laser scans, transforms messages, and generates a probabilistic pose. On startup, amcl initializes its particle filter according to the parameters provided in the setup. If you...

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