A MEMORY-BASED APPROACH TO NAVIGATION

A memory-based system for autonomous indoor navigation is presented. T he system was implemented as a follow-midline reflex on a robot that m oves along the corridors of our institute. The robot estimates its pos ition in the environment by comparing the visual input with images con tained in its memory. Spatial positions are represented by classes. Me mories are formed during a learning phase by encoding labeled images. The output of the system is the a posteriori probability distribution of the classes, given an input image. During performance, an image is assigned to the class that maximizes the probability. This work shows that extensive use of memory can reduce information processing to a si mple and flexible procedure, without the need of complicated and speci fic preprocessing. The system is shown to be reliable, with good gener alization capability. With learning limited to a small part of a corri dor, the robot navigates along the entire corridor. Furthermore, it is able to move in other corridors of different shape, with different il lumination conditions.