![](bg6.png)
filter. We can see how the results obtained with the
Gaussian filter are almost identical to those obtained
using the Butterworth filter.
6 CONCLUSIONS
In this work, we have presented some methods for
the creation of robust dense maps of real
environments, using an appearance-based approach
from previously filtered panoramic images.
We have presented two possible methods for
filtering against illumination changes in the
environment. As shown, the application of the first
method (edge detection), not only does not improve
but also worsens the results. On the other hand,
applying a homomorfic filter on the panoramic
image significantly improves the localization. Very
similar results are obtained when constructing the
homomorfic filter using a Gaussian filter or using a
Butterworth filter. Furthermore, we have tuned the
parameters of the filters to obtain a robust location
against changes in illumination.
We have built the database by applying a
compression of the visual information. We have
used the Fourier signature due to the fact that it
presents better results in terms of amount of memory
and computation times needed to build the database.
It is also important the fact that it presents
orientation invariance and it allow us to compute the
robot orientation. Finally, an important property is
that the Fourier transform is an inherently
incremental method. These properties make it
possible to be applied in future works where robots
have to add new information to the map and localize
simultaneously in real time.
This work opens the door to the use of
appearance-based methods with applications in
mobile robots. As we have shown, the map created
is robust against changes of lighting conditions, and
it permits thus to recover the location and orientation
of the robot in the map even if there are changes in
the illumination of the scene.
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’.
REFERENCES
Adini, Y., Moses, I., Ullman, S., 1997. Face recognition:
the problem of compensating for changes in
illumination direction. In IEEE Trans.Pattern Analysis
and Machine Intelligence, Vol. 19, No. 7, pp. 721-732.
Bischof, H., Wildenauer, H., Leonardis, A., 2004.
Illumination intensitive recognition using eigenspaces.
In ELSEVIER Computer Vision and Image
Understanding, Vol. 95, No. 1, pp. 86-104.
De Araujo, V., Maia, R., D’Angelo, M., D’Angelo, G.,
2006. Automatic Plate Detection using genetic
algorithm. In Proc. World Scientific and Engineering
Academy and Society, pp. 43-48.
Faraid, H., Adelson, E., 1999. Separating reflections and
lighting using independent components análisis. In
Proc. IEEE Computer Society Conf. Computer Vision
and Pattern Recognition. Vol. 1, pp. 1262-1267.
Gil, A., Martinez, O., Ballesta, M., Reinoso, O., 2009. A
comparative evaluation of interest point detectors and
local descriptors for visual SLAM. In SPRINGER
Machine Vision and Applications.
Gonzalez, R.C., Woods, R.E., 1993. Digital Image
Processing. Ed. Addison Wesley.
Jogan, M., Leonardis, A., 2000. Robust Localization
Using Eigenspace of Spinning-Images. In Proc. IEEE
Workshop on Omnidirectional Vision, Hilton Head
Island, USA, pp. 37-44, IEEE.
Kröse, B., Bunschoten, R., Hagen, S., Terwijn, B.
Vlassis, N., 2004. Household robots: Look and learn.
In IEEE Robotics & Automation magazine. Vol. 11,
No. 4, pp. 45-52.
Menegatti, E.; Maeda, T. Ishiguro, H., 2004. Image-based
memory for robot navigation using properties of
omnidirectional images. In Robotics and Autonomous
Systems. Vol. 47, No. 4, pp. 251-276.
Murase, H., Nayar, S., 1994. Illumination planning for
object recognition using parametric eigenspaces. In
IEEE Trans. Pattern Anal. Mach. Intell., Vol 16, pp.
1219-1227.
Payá, L., Reinoso, O., Gil, A., Sogorb. J., 2008. Multi-
robot route following using omnidirectional vision and
appearance-based representation of the environment.
In Lecture Notes in Artificial Intelligence. Vol. 5271,
pp. 680-687 Springer.
Payá, L., Fernández, L., Reinoso, O., Gil, O., Úbeda, D.,
2009. Appearance-based dense maps creation.
Comparison of compression techniques with
panoramic images. In Int. Conf. on Informatic in
Control, Automation and Robotic, Vol. 6, pp. 238-246.
Rossi, F., Ranganathan, A., Dellaert, F., Menegatti, E.,
2008. Toward topological localization with spherical
Fourier transform and uncalibrated camera. In Proc.
Int. Conf. on Simulation, Modeling and Programming
for Autonomous Robots. Venice (Italy), pp. 319-330.
Ueonara, M., Kanade, T, 1998. Optimal approximation of
uniformly rotated images: relationship between
Karhunen-Loeve expansion and Discrete Cosine
Transform. In IEEE Trans. Image Processing. Vol. 7,
No. 1, pp. 116-119.
ICAART 2010 - 2nd International Conference on Agents and Artificial Intelligence
228