New algorithms for detecting and interpreting linear features of a real scene as imaged by a single camera on a mobile robot are described. The low-level processing stages are specifically designed to increase the usefulness and the quality of the extracted features for indoor scene understanding. In order to derive 3-D information from a 2-D image, we consider only lines with particular orientation in 3-D. The detection and interpretation processes provide a 3-D orientation hypothesis for each 2-D segment. This in turn is used to estimate the robot's orientation and relative position in the environment. Next, the orientation data is used by a motion stereo algorithm to fully estimate the 3-D structure when a sequence of images becomes available. From detection to 3-D estimation, a strong emphasis is placed on real-world applications and very fast processing with conventional hardware. Results of experimentation with a mobile robot under realistic conditions are given and discussed.>
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Lebegue et al. (1993) studied this question.
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