Book Image

Effective Robotics Programming with ROS - Third Edition

By : Sánchez, Fernandez Perdomo, Mahtani
Book Image

Effective Robotics Programming with ROS - Third Edition

By: Sánchez, Fernandez Perdomo, Mahtani

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 (12 chapters)
11
Index

Segmentation


Segmentation is the process of partitioning a dataset into different blocks of data satisfying certain criteria. The segmentation process can be done in many different ways and with varied criteria; sometimes, it may involve extracting structured information from a point cloud based on a statistical property, and in other cases, it can simply require extracting points in a specific color range.

In many cases, our data might fit a specific mathematical model, such as a plane, line, or sphere, among others. When this is the case, it is possible to use a model estimation algorithm to calculate the parameters for the model that fits our data. With those parameters, it is then possible to extract the points belonging to that model and evaluate how well they fit it.

In this example, we are going to show how to perform model-based segmentation of a point cloud. We are going to constrain ourselves to a planar model, which is one of the most common mathematical models you can usually...