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

5.1. Camera Calibration 57
Later, in order to solve this problem, we came up increasing the size of
the ball. Again, this trial did not resulted as we expected, since when the
ball gets further from the robot, the diameter information is not reliable.
This results to calculate a wrong value of the ball position. In this trials, we
realized also the effects of the white balance and exposure compensation for
the color information. The camera tries to automatically adjust the color
information as the ball gets further from the light source. Since the blob
elimination is dependent on both the shape of the ball and the color of the
blob, the information changed by the camera results to definition of wrong
blob information. To clarify with an example, the ball shape is checked by
the circularity information calculated by the camera, but the whole ball is
not composed of the same color, since the light is affecting the color, so the
center can be more bright or the corner more dark etc. The camera with the
automatic setting of the white balance and exposure compensation tends to
change the color in order to have a better picture, but by doing so the color
temperature is changed. So the blob search eliminates the non-color points
as well as the non-shape objects and detects the ball in another shape, such
as an open rectangle. This whole change also results to a change of the
diameter of the ball and the distance information is not calculated correctly.
Thediameter of theball is a problematic issuedueto the low-cost optics.
Instead, we decided to change our ball into a cylindric shape and assume
that the object will always be on the ground. This reduces the mapping to
a 2 D to 2 D mapping problem, since Z (the height) will be always zero.
Instead of calculating the diameter of theball, we determinethedistance between
the end of the image and the intersection point of the object with the
ground. This distance is easier to measure, independently from the shape
of the object, and will always change as the object’s position changes in the
world. The mentioned mapping also makes easier the blob search, since it
is not very important to detect the object clearly as a cylindric. If we can
recognize some part of the object we can ensure that the object is more or
less close to the calculated point. This also gave us the flexibility for the
colorinformation, sincewedonothavetheobligationtodetectalltheobject,
we can defineamore generic color code that is working for most of the cases.
We used the second configuration in both camera positions. Even the
internal camera parameters are always same in our camera, the external
parameters are effected with a translation or rotation. Indeed, when the
camera head position is changed, it results with the usage of a H matrix for
each transformation.