Typicality problem refers to the identification of new classes in a general classification context. This typicality concept is used in this paper to help a robot acquiring a topological representation of the environment during its exploration phase. We describe a robust place recognition algorithm that fuses a number of uncertain local matches into a high-confidence global match. The problem is addressed using INCA statistic which follows a distance-based approach. Experiments performed in simulation and in a real robot/environment system show the adequateness of the approach.
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