The image Jacobian depends not only on the image plane coordinates but also the distance from the camera to the points of interest. If this distance is not known, what can we do? Let’s look at how we can determine this distance, and how the optical flow equation can be rearranged to convert from observed […]
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Visual servoing is concerned with the motion of points in the world. How can we reliably detect such points using computer vision techniques.
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.
We use MATLAB and some Toolbox functions to create a robot controller that moves a camera so the image matches what we want it to look like. We call this an image-based visual servoing system.
We live in a three-dimensional world but it’s taken humans a long time to learn how to realistically depict the illusion of depth on a flat surface.
It is common to think about an assembly task being specified in terms of coordinates in the 3D world. An alternative approach is to consider the task in terms of the relative position of objects in one or more views of the task — visual servoing.
Building a highly accurate robot is not trivial yet we can perform fine positioning tasks like threading a needle using hand-eye coordination. For a robot we call this visual servoing.
In order to determine the size and distance of objects in the scene our brain uses a number of highly evolved tricks. Let’s look at some of these.
When a camera moves in the world, points in the image move in a very specific way. The image plane or pixel velocity is a function of the camera’s motion and the position of the points in the world. This is known as optical flow. Let’s explore the link between camera and image motion.
Let’s recap the important points from the topics we have covered in our discussion of optical flow and visual servoing.