MASTERCLASS

Vision and motion

Lessons

  1. Introduction to Vision and Motion
  2. Vision-based Robot Control
  3. Specifying 3D Tasks in Terms of Images
  4. View From a Moving Camera
  5. The Image Jacobian
  6. Perceptibility and Ambiguity of Camera Motion
  7. Image-Based Visual Servoing
  8. Point Correspondence in Image-Based Visual Servoing
  9. Optical Flow and Camera Velocity
  10. Features for Visual Servoing
  11. Summary of Vision and Motion

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Transcript

Welcome to the last lecture in the Robotic Vision course and thanks for hanging in there. We've talked a lot about the way that images are formed, how we transform the three-dimensional world into a two dimensional image. And an underlying assumption in all of that has been that the camera is stationary and the object is stationary. 

Well now we're going to consider the case where the camera is moving. The first thing we're going to look at then is how does the image change as the camera moves through space. And then we're going to flip it around and say, let's say that I've got an image of something that looks like this in the image and I want it to look like this in the image. What we're going to try to work out is how should the camera move in order to change the image from this to this. 

The camera has to change its viewpoint and so we want to work out directly how the camera should move in order to make that happen. This is a technique called "Vision Based Control" sometimes called "Visual Servoing" and is a topic that I've spent a good deal of my life thinking about.

Code

There is no code in this lesson.

An important problem in robotic vision is moving a camera so that the view it sees matches the view we want it to have. To achieve this we exploit knowledge about how an image changes as a camera moves. Then we invert that and compute how the camera should move so the image changes in the way we want, a technique commonly known as visual servoing.

Professor Peter Corke

Professor of Robotic Vision at QUT and Director of the Australian Centre for Robotic Vision (ACRV). Peter is also a Fellow of the IEEE, a senior Fellow of the Higher Education Academy, and on the editorial board of several robotics research journals.

Skill level

This content assumes an understanding of high school level mathematics; for example, trigonometry, algebra, calculus, physics (optics) and experience with MATLAB command line and programming, for example workspace, variables, arrays, types, functions and classes.

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Discussion

  1. Shivam Joshi says:
    5 July 2020 at 3:26 pm

    Thank u sir ,this videos where very helpful

  2. rmai says:
    16 November 2020 at 5:47 pm

    Thank you Professor Corke for these great video lecture series, they help me tremendously to understand the basic concepts of robotics and vision.

  3. adamamir says:
    3 March 2021 at 3:31 am

    Thank you Peter, your knowledge and information are great to listen and learn from, and appreciate all the work you’ve done to share this and provide it to others – thank you!

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