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Finding traversable paths using computer vision is one of the most important components of an intelligent mobile robot system. For a wall climbing robot that operates in an urban environment, it is essential to automatically detect surface types and orientations for switching between moving and climbing, and for applying different adhesive forces both to save energy and ensure its own safety. This paper presents a novel segmentation-based stereovision approach in order to rapidly obtain accurate 3D estimations of urban scenes with largely textureless areas and sharp depth changes. The new approach takes advantage of the fact that many man-made objects in an urban setting consist of planar surfaces. Our approach has three main components: extraction of natural (planar) matching primitives, stereo matching via three-step algorithm (global match, local match and plane fitting), and plane merging and parameter refinement. Experimental results are provided for real indoor scenes.