An Integrated Bayesian Approach to Layer Extraction from Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Symmetric Stereo Matching for Occlusion Handling
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Symmetric Patch-Based Correspondence Model for Occlusion Handling
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
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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.