Abstract: A SCene INterpreter system, SCIN, for indoor scenes has been developed as a part of an autonomous navigation project at the University of Oulu. Starting from passive stereo, SCIN gradually forms a scene graph utilizing semantic net representation. In the graph formation, bottom-up and top-down approaches are combined with the exploitation of a priori information from the environment. The interpretation is done by matching model graphs to the scene graph, and gradually refining the scene graph towards a higher level symbolic representation. The refinement is done by balancing between replacement and an addition of a new node. The model graphs represent different kinds of entities the system is supposed to find from a scene. The complexity of the used model graphs grows as the analysis proceeds. This paper describes the structure of the scene graph, and the model graphs, and the benefits of this representation. The matching and refining of the scene graph are also explained. Experimental results using a robot arm and an indoor robot vehicle are presented to verify the operation of the interpreter.!12
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