Recognition of intersection scene by attentive observation for a mobile robot

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This paper describes a method of recognition of intersection scenes by attentive observation considering the uncertainty of recognition, for mobile robots on roads. From a monocular color image, homogeneous color regions are extracted. Probabilities of the regions coming from specific objects are calculated. From these probabilities and the relationship between these objects and intersection types, the current probability distribution of intersection types is calculated. If the entropy of the probability distribution is lower than a certain threshold, the robot adopts the best hypothesis. Otherwise, the robot selects and observes the part which can minimize the expectation of the entropy attentively. These actions are iterated until the entropy becomes lower than the threshold. The experimental results are shown for actual intersection scenes including a white mark, a curve mirror and so on.

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