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omnidirectional images and appearance-based
methods. We have presented three different methods
to compress the information in the map. The
mathematical properties of these methods together
with the rich information the omnidirectional images
pick up from the environment permit the robot to
compute its position and orientation into the map.
The Fourier Transform method (both the 2D
Discrete Fourier Transform and the Fourier
Signature) has proved to be a good method to
compress the information comparing to PCA
regarding both the time and the amount of memory,
and the accuracy in position and orientation
estimation. Another important property is that the
Fourier Transform is an inherently incremental
method. When we work with PCA, we need to have
all the training images available before carrying out
the compression so this method cannot be applied to
tasks that require an incremental process (e.g. a
SLAM algorithm where the information of the new
location must be added to the map while the robot is
moving around the environment). The Fourier
Transform does not present this disadvantage
because the compression of each image is carried
out independently. These properties make it
applicable to future tasks where the robots have to
add new information to the map and localize
themselves in real time.
This work opens the door to new applications of
the appearance-based methods in mobile robotics.
As we have shown, the main problem these methods
present is the high requirements of memory and
computation time to build the database and make the
necessary comparisons to compute the position and
orientation of the robot. Once we have studied in
deep some methods to compress the information and
separate the calculation of position and orientation,
the next step should be to test their robustness to
changes in illumination and in the position of some
objects in the scene. Also, their robustness and
simplicity make them applicable to the creation of
more sophisticated maps, where we have no
information of the position the robot had when he
took the training images.
ACKNOWLEDGEMENTS
This work has been supported by the Spanish
government through the project DPI2007-61197.
‘Sistemas de percepción visual móvil y cooperativo
como soporte para la realización de tareas con redes
de robots’.
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APPEARANCE-BASED DENSE MAPS CREATION - Comparison of Compression Techniques with Panoramic Images
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