一种用于不同环境下门检测的自组织神经网络方法
A Self-Organizing Neural Scheme for Door Detection in Different Environments
摘要：
门是室内移动机器人导航的重要标志，也是帮助盲人独立进入不熟悉的建筑物。现有的大多数门检测算法都局限于熟悉的环境，因为有限的假设的颜色，纹理和形状。在本文中，我们提出了一种新的方法，采用基于特征的分类，并使用Kohonen自组织映射（SOM）的门检测的目的。通用和稳定的功能用于SOM的训练，显着提高性能：边缘，底部边缘强度剖面和门边缘。为了验证我们的方法的鲁棒性和通用性，我们收集了来自各种环境和不同照明条件的真实世界门图像的大型数据集。该算法实现了超过95%的检测，这表明我们的门检测方法是通用的和鲁棒的颜色，纹理，遮挡，光照条件，尺度和视点的变化。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
