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This paper describes new algorithms for detecting and interpreting linear features of a real scene as imaged by a single camera on a mobile robot. The lowlevel processing stages are specifically designed to increase the usefulness and the quality of the extracted features for a semantic interpretation. 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 and to delimit the free space visible in the image. 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 t o 3-D estimation, a strong emphasis is placed on real-world applications and very fast processing with conventional hardware.

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