Book Image

Effective Robotics Programming with ROS - Third Edition

By : Anil Mahtani, Luis Sánchez, Aaron Martinez, Enrique Fernandez Perdomo
Book Image

Effective Robotics Programming with ROS - Third Edition

By: Anil Mahtani, Luis Sánchez, Aaron Martinez, Enrique Fernandez Perdomo

Overview of this book

Building and programming a robot can be cumbersome and time-consuming, but not when you have the right collection of tools, libraries, and more importantly expert collaboration. ROS enables collaborative software development and offers an unmatched simulated environment that simplifies the entire robot building process. This book is packed with hands-on examples that will help you program your robot and give you complete solutions using open source ROS libraries and tools. It also shows you how to use virtual machines and Docker containers to simplify the installation of Ubuntu and the ROS framework, so you can start working in an isolated and control environment without changing your regular computer setup. It starts with the installation and basic concepts, then continues with more complex modules available in ROS such as sensors and actuators integration (drivers), navigation and mapping (so you can create an autonomous mobile robot), manipulation, Computer Vision, perception in 3D with PCL, and more. By the end of the book, you’ll be able to leverage all the ROS Kinetic features to build a fully fledged robot for all your needs.
Table of Contents (18 chapters)
Effective Robotics Programming with ROS Third Edition
About the Authors
About the Reviewer
Customer Feedback


In this chapter, we have explored the different tools, algorithms, and interfaces that can be used to work with point clouds in ROS. The reader might have noticed that we have tried to link the examples together to provide more insight into how these kinds of nodes might be used in a reusable manner. In any case, given the computational price of point cloud processing, any kind of architectural design will be inextricably linked to the computational capabilities of the system at hand.

The data flow of our examples should start with all the data producers, which are pcl_create and pcl_read. It should continue to the data filters, which are pcl_filter and pcl_downsampling. After the filtering is performed, more complex information can be extracted through pcl_planar_segmentation, pcl_partitioning and pcl_matching. Finally, the data can be written to disk through pcl_write or visualized through pcl_visualize.

The main objective of this particular chapter was to provide clear and concise...