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Doors are important landmarks for indoor mobile robot navigation and alsoassist blind people to independently access unfamiliar buildings. Most existingalgorithms of door detection are limited to work for familiar environmentsbecause of restricted assumptions about color, texture and shape. In this paperwe propose a novel approach which employs feature based classification and usesthe Kohonen Self-Organizing Map (SOM) for the purpose of door detection.Generic and stable features are used for the training of SOM that increase theperformance significantly: concavity, bottom-edge intensity profile and dooredges. To validate the robustness and generalizability of our method, wecollected a large dataset of real world door images from a variety ofenvironments and different lighting conditions. The algorithm achieves morethan 95% detection which demonstrates that our door detection method is genericand robust with variations of color, texture, occlusions, lighting condition,scales, and viewpoints.

A Self-Organizing Neural Scheme for Door Detection in Different Environments