This paper describes a vision system for obstacle detection in mobile robot navigation. The system uses an image processing board equipped with an MPEG motion estimation processor that calculates a robust optic-flow-like vector field in real-time. Algorithms running in software on the host PC then evaluate this field. As the solutions to the general problem of structure and motion from optic flow are too instable for the use in this application, the typical constraints of mobile robotics are exploited, i.e. a reduced set of motion parameters and a known ground plane. Ego-motion can then be reconstructed with robust one-dimensional methods. A new criterion for obstacles that copes well with the noise properties of the motion field is introduced. For vectors, belonging to obstacles the 3D information is reconstructed allowing not only qualitative detection of obstacles but quantitative path planning.