Modeling unknown environments with a mobile robot

The exploration of an unknown environment is an important task for the new generation of mobile service robots. These robots are supposed to operate in dynamic and changing environments together with human beings and other static or moving objects. Sensors that are capable of providing the quality of information that is required for the described scenario are optical sensors like digital cameras and laserscanners. In this paper sensor integration and fusion for such sensors is described. Complementary sensor information is transformed into a common representation in order to achieve a cooperating sensor system. Sensor fusion is performed by matching the local perception of a laserscanner and a camera system with a global model that is being built up incrementally. The Mahalanobis-distance is used as matching criterion and a Kalman-filter is used to fuse matching features. A common representation including the uncertainty and the confidence is used for all scene features. The system's performance is demonstrated for the task of exploring an unknown environment and incrementally building up a geometrical model of it.