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Effective Robotics Programming with ROS

You're reading from   Effective Robotics Programming with ROS Find out everything you need to know to build powerful robots with the most up-to-date ROS

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Product type Paperback
Published in Dec 2016
Publisher Packt
ISBN-13 9781786463654
Length 468 pages
Edition 3rd Edition
Tools
Concepts
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Authors (3):
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Luis S√°nchez Luis S√°nchez
Author Profile Icon Luis S√°nchez
Luis S√°nchez
Enrique Fernandez Perdomo Enrique Fernandez Perdomo
Author Profile Icon Enrique Fernandez Perdomo
Enrique Fernandez Perdomo
Anil Mahtani Anil Mahtani
Author Profile Icon Anil Mahtani
Anil Mahtani
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Toc

Table of Contents (12) Chapters Close

Preface 1. Getting Started with ROS FREE CHAPTER 2. ROS Architecture and Concepts 3. Visualization and Debugging Tools 4. 3D Modeling and Simulation 5. The Navigation Stack – Robot Setups 6. The Navigation Stack – Beyond Setups 7. Manipulation with MoveIt! 8. Using Sensors and Actuators with ROS 9. Computer Vision 10. Point Clouds Index

Adaptive Monte Carlo Localization


In this chapter, we are using the Adaptive Monte Carlo Localization (AMCL) 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, the amcl node initializes its particle filter according to the parameters provided in the setup...

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