Extracting Minimalistic Corridor Geometry from Low-Resolution Images

Abstract. We propose a minimalistic corridor representation consisting of the orientation line (center) and the wall-floor boundaries (lateral limit). The repre-sentation is extracted from low-resolution images using a novel combination of information theoretic measures and gradient cues. Our study investigates the im-pact of image resolution upon the accuracy of extracting such a geometry, show-ing that accurate centerline and wall-floor boundaries can be estimated even in texture-poor environments with images as small as 16 × 12. In a database of 7 unique corridor sequences for orientation measurements, less than 2 % additional error was observed as the resolution of the image decreased by 99%. One of the advantages of working at such resolutions is that the algorithm operates at hun-dreds of frames per second, or equivalently requires only a small percentage of the CPU