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ROS Robotics Projects

You're reading from   ROS Robotics Projects Make your robots see, sense, and interact with cool and engaging projects with Robotic Operating System

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781783554713
Length 452 pages
Edition 1st Edition
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Author (1):
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Lentin Joseph Lentin Joseph
Author Profile Icon Lentin Joseph
Lentin Joseph
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Toc

Table of Contents (13) Chapters Close

Preface 1. Getting Started with ROS Robotics Application Development 2. Face Detection and Tracking Using ROS, OpenCV and Dynamixel Servos FREE CHAPTER 3. Building a Siri-Like Chatbot in ROS 4. Controlling Embedded Boards Using ROS 5. Teleoperate a Robot Using Hand Gestures 6. Object Detection and Recognition 7. Deep Learning Using ROS and TensorFlow 8. ROS on MATLAB and Android 9. Building an Autonomous Mobile Robot 10. Creating a Self-Driving Car Using ROS 11. Teleoperating a Robot Using a VR Headset and Leap Motion 12. Controlling Your Robots over the Web

Deep learning for robotics


Here are the main robotics areas where we apply deep learning:

  • Deep-learning-based object detector: Imagine a robot wants to pick a specific object from a group of objects. What could be the first step for solving this problem? It should identify the object first, right? We can use image processing algorithms such as segmentation and Haar training to detect an object, but the problem with those techniques is they are not scalable and can't be used for many objects. Using deep learning algorithms, we can train a large neural network with a large dataset. It can have good accuracy and scalability compared to other methods. Datasets such as ImageNet (http://image-net.org/), which have a large collection of image datasets, can be used for training. We also get trained models that we can just use without training. We will look at an ImageNet-based image recognition ROS node in an upcoming section.

  • Speech recognition: If we want to command a robot to perform some task...

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