object recognition in ros using feature extractors and feature matchers by dolev shapira

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Object Recognition in ROS Using Feature Extractors and Feature Matchers By Dolev Shapira

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Page 1: Object Recognition in ROS Using Feature Extractors and Feature Matchers By Dolev Shapira

Object Recognition in ROS

Using Feature Extractors

and Feature MatchersBy Dolev Shapira

Page 2: Object Recognition in ROS Using Feature Extractors and Feature Matchers By Dolev Shapira

Introduction and Goals

• Provide a reliable, modular system for object recognition.

• Provide guidance for the user how to optimize the use of the system.

• Creation of a “standalone” node that provides an of abstraction between object recognition and other processes.

Page 3: Object Recognition in ROS Using Feature Extractors and Feature Matchers By Dolev Shapira

Introduction to ROS

“What is ROS?The Robot Operating System (ROS) is a set of software libraries and tools that help you build robot applications. From drivers to state-of-the-art algorithms, and with powerful developer tools, ROS has what you need for your next

robotics project. And it's all open source”.

ROS.org

Page 4: Object Recognition in ROS Using Feature Extractors and Feature Matchers By Dolev Shapira

Introduction to ROS

ROS provides an interface that allows the user to create a modular independent pieces of code called “Nodes”, communicating with each other

using “Topics .”

Page 5: Object Recognition in ROS Using Feature Extractors and Feature Matchers By Dolev Shapira

Introduction to ROS

• Nodes – “processes”.• Topics – “message boards”.• Subscribers – Nodes “watching” the topics

and reading incoming messages.• Publishers – Nodes publishing (“posting”)

messages into topics.

Page 6: Object Recognition in ROS Using Feature Extractors and Feature Matchers By Dolev Shapira

OpenCV

OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products.

Page 7: Object Recognition in ROS Using Feature Extractors and Feature Matchers By Dolev Shapira

Feature Detection

We have seen in this course a biological mechanism used for feature detection.•Simple cells – Orientation.•Complex cells – Orientation and Direction.•Hypercomplex cells – “end-stop” property.The only question in hand is, what feature should we trace?

Page 8: Object Recognition in ROS Using Feature Extractors and Feature Matchers By Dolev Shapira

SIFT Feature Detector

• Scale invariant.• Rotation invariant.• Functions well even when there is a change in

illumination.• Function relatively well even when there’s

noise.David G. Lowe 2004

Page 9: Object Recognition in ROS Using Feature Extractors and Feature Matchers By Dolev Shapira

How Does it Work?

• We load a set of “Templates” prepared beforehand.

• We sample images from the camera (scenes).• We detect features both in the scene, and the

templates.• We match between the two using a Matcher.

Page 10: Object Recognition in ROS Using Feature Extractors and Feature Matchers By Dolev Shapira

Templates

• Should not be noisy.• Should not contain a transparent part of an

object.• Should not contain a reflective part of an

object.• Should contain “static” features.• Should be of a part proportional to the object

itself (with relation to the resolution).

Page 11: Object Recognition in ROS Using Feature Extractors and Feature Matchers By Dolev Shapira

Results

Page 12: Object Recognition in ROS Using Feature Extractors and Feature Matchers By Dolev Shapira

Results