基于二维GDSOM的路标动态自组织提取方法

提出一种基于距离传感器的结构化特征的动态、自组织提取方法。该方法由3个部分组成:主动感知行为的设计，时空信息的降维处理及路标的自组织提取。设计基于沿墙走的“主动感知行为”来获得高相关性的感知时空序列信息;给出基于变化检测和激活强度的活性神经元来对时空序列信息降维;最后提出一种二维动态增长自组织特征图方法，实现环境路标的自组织提取和识别。实验结果验证该方法的有效性。

Dynamic Self-Organizing Landmark Extraction Method Based on 2-Dimensional Growing Dynamic Self-Organizing Feature Map

A dynamic self-organizing structural feature extraction method is presented based on distance sensor. The procedure consists of three parts: design of active exploration behavior, dimensionality reduction process of spatio-temporal information and self-organizing landmark extraction method. In this paper, active exploration behavior based on follow-wall is designed to obtain high correlative spatio-temporal sequence information. Activity neurons based on variety detection and activation intensity are used to reduce the dimensionality of spatio-temporal sequence. Finally, a method of 2-Dimensional growing dynamic self-organizing feature map (2-Dimensional GDSOM) is proposed to achieve self-organizing extraction and identification of environmental landmarks. The experimental results demonstrate the effectiveness of the method.