A Self-Organizing Neural Scheme for Door Detection in Different Environments
This addresses a practical problem for robotics and accessibility, but it is incremental as it builds on existing feature-based methods with a specific neural scheme.
The paper tackles the problem of door detection for indoor mobile robot navigation and assisting blind people by proposing a feature-based classification approach using a Kohonen Self-Organizing Map, achieving over 95% detection accuracy across varied environments and conditions.
Doors are important landmarks for indoor mobile robot navigation and also assist blind people to independently access unfamiliar buildings. Most existing algorithms of door detection are limited to work for familiar environments because of restricted assumptions about color, texture and shape. In this paper we propose a novel approach which employs feature based classification and uses the Kohonen Self-Organizing Map (SOM) for the purpose of door detection. Generic and stable features are used for the training of SOM that increase the performance significantly: concavity, bottom-edge intensity profile and door edges. To validate the robustness and generalizability of our method, we collected a large dataset of real world door images from a variety of environments and different lighting conditions. The algorithm achieves more than 95% detection which demonstrates that our door detection method is generic and robust with variations of color, texture, occlusions, lighting condition, scales, and viewpoints.