Page 186 - Introduction to Autonomous Mobile Robots

Perception

2
G ≅ s + s 2 ; 171
1 2
–
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– 1 2 1 –0 1
–
s
θ ≅ atan  ; s = 000 ; s = –0 2 (4.87)
1
----
2

1
2
s
2
1
121 –0 1
Dynamic thresholding. Many image-processing algorithms have generally been tested in
laboratory conditions or by using static image databases. Mobile robots, however, operate
in dynamic real-world settings where there is no guarantee regarding optimal or even stable
illumination. A vision system for mobile robots has to adapt to the changing illumination.
Therefore a constant threshold level for edge detection is not suitable. The same scene with
different illumination results in edge images with considerable differences. To dynamically
adapt the edge detector to the ambient light, a more adaptive threshold is required, and one
approach involves calculating that threshold based on a statistical analysis of the image
about to be processed.
To do this, a histogram of the gradient magnitudes of the processed image is calculated
(figure 4.47). With this simple histogram it is easy to consider only the pixels with the
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highest gradient magnitude for further calculation steps. The pixels are counted backward
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starting at the highest magnitude. The gradient magnitude of the point where is reached
will be used as the temporary threshold value.
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The motivation for this technique is that the pixels with the highest gradient are
expected to be the most relevant ones for the processed image. Furthermore, for each
image, the same number of relevant edge pixels is considered, independent of illumination.
It is important to pay attention to the fact that the number of pixels in the edge image deliv-
ered by the edge detector is not . Because most detectors use nonmaxima suppression, the
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number of edge pixels will be further reduced.
Straight edge extraction: Hough transforms. In mobile robotics the straight edge is
often extracted as a specific feature. Straight vertical edges, for example, can be used as
clues to the location of doorways and hallway intersections. The Hough transform is a
simple tool for extracting edges of a particular shape[16, 18]. Here we explain its applica-
tion to the problem of extracting straight edges.
Suppose a pixel x y,( p p ) in the image is part of an edge. Any straight-line edge includ-
I
ing point x y,( p p ) must satisfy the equation: y = m x + b 1 . This equation can only be
1 p
p
satisfied with a constrained set of possible values for m 1 and b 1 . In other words, this equa-
tion is satisfied only by lines through I that pass through x y,( . )
p p