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Feature-based image segmentation for indoor robot-navigation

This contribution presents an architecture, which enables collision-avoiding robot navigation in indoor-environments based on monocular visual information. This is achieved by neural linkage of image features with depth information, which can be extracted from the stream of visual data. A first approach only segments camera images into obstacle and passable underground regions with the exclusive help of suitably trained neural gases. An extended approach is introduced that merges the results of different segmenting procedures into a stable result. In this paper the efficiency of the algorithm will be described, problems in the real world application and their appropriate solutions will be pointed out.