Generic shape recognition is the problem of determining the pose and dimensions of objects for which only topological models are available (that is, the shape of the object is known but the size is unknown). One such application domain is the handling and sorting of postal objects. Because metrical information relating object features to one another is not available, the more common model-based approaches are inadequate. The INGEN (inference engine for generic object recognition) system uses a data-driven approach to recognize objects with generic shapes, such as parallelepipeds and cylinders, when the dimensions of the objects are unknown. Size and pose estimation are facilitated by a geometric reasoning process which extends objects in the direction away from the sensor until they physically contact other objects in the scene. The results of experiments which use the INGEN system to guide a robot in the manipulation of postal objects are presented.>

A major hurdle in the development of intelligent robots is that we still do not possess efficient computational and representational methodologies for emulating knowledge and expectation driven behavior so basic to human cognition and problem solving. Even if we use techniques such as geometric modeling for representing objects in the robot world, we are still lacking in methods for linking such representations with sensory feedback. In this paper, we have proposed the use of intermediate representations - we call them sensor-tuned representations - for linking CSG based solid modeling with sensory information. We also discuss how sensor-tuned representations are constructed from range data and how object recognition can be done with sensor-tuned representations. Finally, we show results of manipulation experiments produced by the current implementation of the system.