Abstract:

This paper presents a vision-based approach
for mobile robot localization. The model of the environment
is topological. The new approach characterize a
place using a signature. This signature consists of a constellation
of descriptors computed over dierent types
of local affine covariant regions extracted from an omnidirectional
image acquired rotating a standard camera
with a pan-tilt unit. This type of representation permits
a reliable and distinctive environment modeling.
Our objectives were to validate the proposed method
in indoor environments and, also, to nd out if the
combination of complementary local feature region detectors
improves the localization versus using a single
region detector. Our experimental results show that if
false matches are efectively rejected, the combination
of dierent covariant affine region detectors increases
notably the performance of the approach by combining
the dierent strengths of the individual detectors.
In order to reduce the localization time, two strategies
are evaluated

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