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This work, inspired by the idea of "Computing with Words and Perceptions"proposed by Zadeh in 2001, focuses on how to transform measurements intoperceptions for the problem of map building by Autonomous Mobile Robots. Wepropose 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 andintegrate these maps are expressed by linguistic descriptions and modeled byfuzzy rules. The main difference of this approach from other studies reportedin the literature is that the method presented here is based on the hypothesisthat the concepts "occupied" and "empty" are antonyms rather than complementary(as it happens in probabilistic approaches), or independent (as it happens inthe previous fuzzy models).  Controlled experimentation with a real robot in three representative indoorenvironments has been performed and the results presented. We offer aqualitative and quantitative comparison of the estimated maps obtained by theprobabilistic approach, the previous fuzzy method and the new antonyms-basedfuzzy approach. It is shown that the maps obtained with the antonyms-basedapproach are better defined, capture better the shape of the walls and of theempty-spaces, and contain less errors due to rebounds and short-echoes.Furthermore, in spite of noise and low resolution inherent to the sonar-sensorsused, the maps obtained are accurate and tolerant to imprecision.

Approximate Robotic Mapping from sonar data by modeling Perceptions with Antonyms