Page 171 - Designing Autonomous Mobile Robots : Inside the Mindo f an Intellegent Machine

Chapter 11

The number of points remaining that represent a flat surface is one indication of the
image quality in this example. For example, if we end up with 98 out of 100 points,
then we have a very high confidence in the data. However, if we end up with, for
example, 10 points, there is a chance that these points are not really the surface, but
just ten random points that lie on a straight line. The average error of the remaining
points along the wall is another indicator of quality. If we throw out 25 points, but
the remaining 75 points are almost perfectly along a line, then this is a positive
quality indicator.

e12 C23
C12 C

P1 r e23
a
(X1,Y1) r r r

d
M12
((X1+X2)/2,(Y1+Y2)/2) P2 P3
(X3,Y3)
(X2,Y2)

Figure 11.7. Calculating image quality for a pillar

Instead of a flat surface, let’s approach a more interesting feature. Figure 11.7 shows
a large building pillar that has been imaged by a lidar system. In this example, the
pillar is a considerable distance from the robot, so pre-filtering has found only three
range points—P1, P2, and P3—that are in the expected vicinity of this feature and
may therefore belong to it. We know the radius “r” of the column from the robot’s pro-
gram, and we know where the column should be, so we begin by processing adjoining
pairs of points.

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