  This work, inspired by the idea of "Computing with Words and Perceptions"
proposed by Zadeh in 2001, focuses on how to transform measurements into
perceptions for the problem of map building by Autonomous Mobile Robots. We
propose to model the perceptions obtained from sonar-sensors as two grid maps:
one for obstacles and another for empty spaces. The rules used to build and
integrate these maps are expressed by linguistic descriptions and modeled by
fuzzy rules. The main difference of this approach from other studies reported
in the literature is that the method presented here is based on the hypothesis
that the concepts "occupied" and "empty" are antonyms rather than complementary
(as it happens in probabilistic approaches), or independent (as it happens in
the previous fuzzy models).
  Controlled experimentation with a real robot in three representative indoor
environments has been performed and the results presented. We offer a
qualitative and quantitative comparison of the estimated maps obtained by the
probabilistic approach, the previous fuzzy method and the new antonyms-based
fuzzy approach. It is shown that the maps obtained with the antonyms-based
approach are better defined, capture better the shape of the walls and of the
empty-spaces, and contain less errors due to rebounds and short-echoes.
Furthermore, in spite of noise and low resolution inherent to the sonar-sensors
used, the maps obtained are accurate and tolerant to imprecision.
