A critical part of a visual servoing system is establishing correspondence between points in the scene observed by the camera, and points in our desired image of the scene.
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We use MATLAB and some Toolbox functions to find corresponding points between two images using SURF features.
Visual servoing is concerned with the motion of points in the world. How can we reliably detect such points using computer vision techniques.
Let’s recap the important points from the topics we have covered in advanced image processing.
For a camera moving through the environment we frequently wish to track particular world points from one frame to the next. We’ll do a quick introduction to the very large field of feature detection and matching using Harris corner features.
We can derive a linear relationship between the coordinates of points on an arbitrary plane in the scene and the coordinate of that point in the image. This is the planar homography and it has a number of everyday uses which might surprise you.
We will consider a very powerful group of functions, spatial operators, where each output pixel is a function of the corresponding input pixel and its neighbours.
Imagine a scene with bright objects against a dark background. Thresholding is a very common monadic operation which transforms the image into one where the pixels have two possible values: true or false which correspond to foreground or background. It can be performed with a single vectorized MATLAB operation.
The orientation of a body in 3D can also be described by a single rotation about a particular axis in space.
We start by considering the effect of gravity acting on a robot arm, and how the torque exerted will disturb the position of the robot controller leading to a steady state error. Then we discuss a number of strategies to reduce this error.