Active markers for outdoor and indoor robot localization

Due to such inaccuracies, the transition between the
two states may shift left or right, resulting in sequences as
for instance 11100011100011000, as it can be seen in
Figure 4.
Figure 4: Actual results from the thresholding algorithm.
Those sequences have different values of n and m for
each ON/OFF period; so, instead of searching for pixels
whose behaviour exactly matches the theoretical sequence,
we assign each pixel a value according to the
weights shown in Figure 5.
The table should be read as follows: if, for a given
pixel, both transitions occur at the correct time, it is assigned
a value of 25. If one transition occurs one frame
earlier or later, the weight of that pixel is 15. If both transitions
are out of timing the weight for that pixel is 5. Any
transition occurring more than one frame earlier or later
than the correct time yields a value of -20. The procedure
is repeated for several cycles, and is similar to integrating
a rectified signal after passing it through a band pass filter.
At the end of this phase we obtain a sort of “values
image” in which the value of every pixel represents how
much that pixel’s pattern matches the behaviour expected
from the marker.
Figure 5: Coefficients used to weigh transitions.
4.4 Blobs detection
The phase just described is performed separately on each
pixel. Pixels with similar behaviour must now be grouped
together, in order to obtain blobs whose centre of gravity
will indicate the geometrical position of the marker. Since
at this stage there normally are several candidates to
choose from, data on the number of pixels, on the maxi-
mum and the medium value of each blob are also collected.
4.5 Marker localization
In this phase, the information collected in the blobs identification
is used to localize the robot.
The centre of gravity of the blob that has the highest
maximum value is chosen as the most probable position
of the marker in the image. If two or more blobs have
similar maximum values, the blob with the highest medium
value is chosen.
5 Experimental results
In order to validate the algorithm two sessions of experiments
were made in outdoor environment. Experiments
were also performed indoor, but they are not reported here
because in that case the task of identifying the marker is
very simple, and no significant experience was gained.
The used marker is a black panel with a circle of high
brightness LEDs as shown in Figure 6. Obviously, such
marker is highly directional, and therefore unsuitable for
being used on a mobile robot. We are currently working
in two directions to solve this problem: a) building the
marker on a hemi-spherical surface to make it omnidirectional,
and b) mounting it horizontally, over a revolving
diffusing mirror, with a device that keeps the device approximately
pointed against the webcam. The latter solution
is more complex, but has the advantage of requiring
less energy and, to some extent, of being less intrusive
because it does not emit unnecessary light.
Figure 6: The marker used in the experiments.
The webcam was set to acquire 15fps. Shutter opening
time was 17ms for all tests. The marker was operated at
13.33Hz, and its “on” time was 33ms. These settings
were chosen to have a beat period between the two signals
of 9 camera frames. A study of the behaviour of the system
led to the conclusion that, with these settings, the
most probable detected sequence is 4 frames where the
marker appears to be ON, and of 5 where it appears to be
OFF. This sequence was considered the most probable
taking into account any possible phase shift between the
marker and the camera, and several other factors, as daylight
effect and noise in captured images. Changes of
these parameters could yield slightly different sequences
of frames but, as it has already been said, the algorithm
has been designed to accommodate such situations.