For mobile robotics, the task of navigation is essential. Navigation comprises selflocalization, planning, and motion. Both self-localization and planning require a representation of the environment, e.g. a metric or topological map. A common framework representing both topological and metric knowledge is the route graph. The proposed project will investigate the integration of spatial knowledge for robot navigation following this approach, especially its potential as a cognitively adequate representation for human-machine-interaction in the navigation domain. Robots are not able to recognize the complete environment; they only perceive a very limited part of it. Using a laser range finder, for example, they only determine the distances to obstacles at a certain height, i.e., they cannot detect objects at other heights, cannot see holes in the ground, etc. Similar constraints apply to the use of existing maps, because they do not contain all objects that may be perceived by the sensors of a robot, e.g. furniture is missing in most maps. In addition, the information perceived by a robot or extracted from a CAD-representation may be inadequate to communicate with human users, because neither a topological nor a metric map is easy to understand without named nodes, edges, or rooms. A route graph may contain this information, but a central question is how it can be acquired and how it will be represented. The project has two goals: on the one hand, pre-existing maps will be semiautomatically adapted to robot navigation, i.e. converted into route graphs, easing the process of map generation and maintenance for robotic applications. On the other hand, route graphs will be augmented by qualitative spatial information resulting from a reduced set of natural language expressions given by an instructor in a spatial context. The instructor shares the reference system with the instructed system, as passenger of a wheelchair, easing the interpretation of the spatial relations the instructions will comprise. Since robotics maps typically consist of metric information while instructions by humans mainly comprise qualitative relations, it is a major goal of the project to combine quantitative and qualitative information for purposes of robot navigation in the long term also for human-machine communication about space.