Book Image

ROS Programming: Building Powerful Robots

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

ROS Programming: Building Powerful Robots

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

Overview of this book

This learning path is designed to help you program and build your robots using open source ROS libraries and tools. We start with the installation and basic concepts, then continue with the more complex modules available in ROS, such as sensor and actuator integration (drivers), navigation and mapping (so you can create an autonomous mobile robot), manipulation, computer vision, perception in 3D with PCL, and more. We then discuss advanced concepts in robotics and how to program using ROS. You'll get a deep overview of the ROS framework, which will give you a clear idea of how ROS really works. During the course of the book, you will learn how to build models of complex robots, and simulate and interface the robot using the ROS MoveIt motion planning library and ROS navigation stacks. We'll go through great projects such as building a self-driving car, an autonomous mobile robot, and image recognition using deep learning and ROS. You can find beginner, intermediate, and expert ROS robotics applications inside! It includes content from the following Packt products: ? Effective Robotics Programming with ROS - Third Edition ? Mastering ROS for Robotics Programming ? ROS Robotics Projects
Table of Contents (37 chapters)
Title page
Copyright and Credits
Packt Upsell
Preface
Bibliography
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 fit...