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Submitted version of the thesis - Airlab, the Artificial Intelligence ...

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5.1. Camera Calibration 53<br />

<strong>the</strong> principal points in terms <strong>of</strong> pixel dimension.<br />

x = K[I|0]Xcam<br />

where Xcam as (X,Y,Z,1) t to emphasize that <strong>the</strong> camera is assumed to be<br />

located at <strong>the</strong> origin <strong>of</strong> a Euclidean coordinate system with <strong>the</strong> principal<br />

axis <strong>of</strong> <strong>the</strong> camera point straight down <strong>the</strong> z-axis, and <strong>the</strong> point Xcam is<br />

expressed in <strong>the</strong> camera coordinate system.<br />

In general, points in <strong>the</strong> space will be expressed in terms <strong>of</strong> a different<br />

Euclidean coordinate frame, known as <strong>the</strong> world coordinate frame. The two<br />

frames are related via a rotation and a translation. The relation between<br />

<strong>the</strong> two frames can be represented as follows:<br />

x = KR[I|−C]X<br />

where X is now in a world coordinate frame. This is <strong>the</strong> general mapping<br />

given by a pinhole camera (In Figure 5.2). A general pinhole camera,<br />

P = KR[I| − C], has a 9 degrees <strong>of</strong> freedom: 3 for K, 3 for R, 3 for C.<br />

The parameters contained in K are called internal camera parameters. The<br />

parameters <strong>of</strong> R and C which relate <strong>the</strong> camera orientation and position to<br />

a world coordinate system are called <strong>the</strong> external parameters. In a compact<br />

form, <strong>the</strong> camera matrix is<br />

where t = −RC.<br />

P = K[R|t]<br />

The camera matrix, which consists <strong>of</strong> internal and external camera parameters,<br />

is calculated by <strong>the</strong> camera calibration toolbox automatically.<br />

Having <strong>the</strong> internal and external camera parameters from <strong>the</strong> toolbox, we<br />

compute <strong>the</strong> homography H. Homography is an invertible transformation<br />

from <strong>the</strong> real projective plane to <strong>the</strong> projective plane.<br />

We developed two different approaches to determine <strong>the</strong> position <strong>of</strong> a<br />

target according to <strong>the</strong> robot coordinates. In <strong>the</strong> first case, <strong>the</strong> target object<br />

is a ball, and we are using <strong>the</strong> information coming from <strong>the</strong> diameter<br />

<strong>of</strong> <strong>the</strong> ball. In this case, <strong>the</strong> transformation should be a 3D to 2D, since <strong>the</strong><br />

ball can be represented in world coordinates system with <strong>the</strong> X,Y,Z and in<br />

<strong>the</strong> camera coordinates with two values X,Y. In <strong>the</strong> second case, we assume<br />

that <strong>the</strong> target object is on <strong>the</strong> ground and we are using <strong>the</strong> information<br />

coming from <strong>the</strong> intersection point <strong>of</strong> <strong>the</strong> object with <strong>the</strong> ground. This

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