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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 FREE CHAPTER 2. Face Detection and Tracking Using ROS, OpenCV and Dynamixel Servos 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

Image recognition using ROS and TensorFlow


After discussing the basics of TensorFlow, let's start discussing how to interface ROS and TensorFlow to do some serious work. In this section, we are going to deal with image recognition using these two.

There is a simple package to perform image recognition using TensorFlow and ROS. Here is the ROS package to do this:

https://github.com/qboticslabs/rostensorflow

This package was forked from https://github.com/OTL/rostensorflow. The package basically contains a ROS Python node that subscribes to images from the ROS webcam driver and performs image recognition using TensorFlow APIs. The node will print the detected object and its probability.

Note

This code was developed using TensorFlow tutorials from the following link: https://www.tensorflow.org/versions/r0.11/tutorials/image_recognition/index.html.

The image recognition is mainly done using a model called deep convolution network. It can achieve high accuracy in the field of image recognition. An...

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