中文摘要：

语义地图构建对移动机器人导航与规划具有重要意义,而环境分类是语义地图构建的核心问题.目前所采用的环境分类方法匹配率较低,已成为语义地图构建所面临的主要问题.对此笔者提出了一种基于支持向量机的分类方法,该方法利用激光雷达数据提取环境几何特征,训练SVM分类器对机器人工作空间模式进行识别,并将所提算法用于室内环境的语义分类.实验结果表明,该分类方法具有较高的识别率,可有效地实现语义地图构建.

英文摘要：

Semantic map building is of significant importance for the navigation and trajectory planning of mobile robots,in which the environmental classification has been a key issue to address.The lower matching rate has been identified in the currently adopted environmental classification methods,which forms a technical bottle-neck for the semantic map building.A classification method based on support vector machine（SVM） is proposed in this paper,In the process,the geometric features extracted from the laser radar data are employed to train the SVM classifier,which is used for identifying he robotic workspace pattern.The proposed method is applied to the semantic classification of the given indoor layout.The experiment results suggest that the proposed method is capable of building the semantic map efficiently with a higher recognition rate.