Constructing maps for mobile robot navigation based on ultrasonic range data

This paper introduces an approach to generating environmental maps based on ultrasonic range data. By means of a learning classifier ultrasonic range data are condensed yielding abstract concepts which enable a mobile robot to discern situations. As a consequence the free-space can be partitioned into situation areas which are defined as regions wherein a specific situation can be recognized. Using dead-reckoning such situation areas can be attached to graph nodes generating a map of the free-space in the form of a graph representation. How the extended Kalman filter algorithm can be applied in this context to compensate the dead-reckoning drift is also discussed.

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