The task of an autonomous vehicle is to travel from one specific location to another with no external assistance. To perform this task, the "brain" of the vehicle reasons by using data from different types of sensor. In this paper we focus on data from a CCD camera on board an autonomous robot. We discuss a neural network approach for form perception and object classification used to control the navigation of the robot. The system provides a just-in-time recognition of markers and signs placed in a partially unknown environment, and enables the robot to perform the traditional goal-directed navigation in typical office environments. After a discussion of the architecture of the perceptual system and of the underlying computational model, we present some preliminary experimental results.
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