A mobile robot that autonomously functions in a complex and previously unknown indoor environment has been developed. The omnidirectional mobile robot uses stereo vision, odometry, and contact bumpers to instantiate a symbolic world model. Finding stereo correspondences across a single epipolar line is adequate for instantiating the model. Uncertainty in sensor data is represented by a multivariate normal distribution, and uncertainty models for motion and stereo are presented. Uncertainty is reduced by extended Kalman filtering. To execute a high-level command such as `Enter the second door on the left', a model is instantiated from sensing and motions are planned and executed. Experimental results from the fast, running system are presented

Soft modeling, stereo vision, motion planning, uncertainty reduction, image processing, and locomotion enable the Mobile Autonomous Robot Stanford to explore a benign indoor environment without human intervention. The modeling system describes rooms in terms of floor, walls, hinged doors and allows for unspecified obstacles. Image processing basically extracts vertical edges along the horizon using an edge appearance model. Stereo vision matches those edges using edge and grey level similarity, constraint propagation and a preference for epipolar ordering. The motion planner tries to move in a way that is likely to increase knowledge about obstacle free space. Results presented are from an autonomous run that included difficult passages such as navigation around a pillar without apriori knowledge.

A mobile robot architecture must include sensing, planning, and locomotion which are tied together by a model or map of the world based on sensor information, apriori knowledge and generic models. The architecture of a Stanford's autonomous mobile robot is described including its distributed computing system, locomotion, and sensing. Additionally, some of the issues in the representation of a world model are explored. Sensor models are used to update the world model in a uniform manner, and uncertainty reduction is discussed.

Es wird ein neuer allgemeiner Ansatz für das Korrespondenzproblem bei Stereo-Bildfolgen vorgestellt. Der Ansatz basiert auf einem flexiblen Systemkonzept, das heuristisches Wissen und Kontrollrahmen strikt trennt und die Integration von Tiefenhinweisen aus Messungen mit anderen Sensoren erlaubt. Das Verfahren verwendet lokale Bildmerkmale und löst Mehrdeutigkeiten durch “constraint propagation” entlang Kanten und eine geeignete Zuordnungsreihenfolge, die anhand einer Gruppierung und Bewertung von Merkmalen innerhalb der Bilder geplant wird. Es werden erste Ergebnisse mit einer Teilimplementation beschrieben, die die planende Komponente des Systems realisiert.