Model-driven vision for in-door navigation

For navigation in a partially known environment it is possible to provide a model that may e used or guidance in the navigation and as a basis for selective sensing. In this paper a navigation system for an autonomous mobile robot is presented. Both navigation and sensing is build around a graphics model, which enables prediction of the expected scene content. The model is used directly for prediction of line segments which through matching allow estimation of position and orientation. In addition the model is used as a basis for a hierarchical stereo matching that enables dynamic updating of the model with unmodelled objects in the environment. For short-terms path planning a set of reactive behaviours are used. The reactive behaviours include use of inverse perspective mapping for generation of occupancy grids, a sonar system and simple gaze holding for monitoring of dynamic obstacles. The full system and its component processes are described and initial experiments with the system are briefly outlined.