基于概念空间学习认知的机器人目标识别方法

针对复杂环境中机器人多类目标识别通常所依托的原始特征空间中数据区分度低、难以直接表达高层次特征知识的挑战性问题,采用概念空间知识表达方法,通过多传感器数据融合与特征提取建立基本特征空间,并运用高斯混合模型表示目标的各种属性,形成具有高层知识特性的概念空间,在此基础上进行高层知识的概念学习,增强多类目标在概念空间中的可区分性.利用支持向量机作为机器人的分类器,实现针对室内环境的多类目标物体的准确识别.实验结果表明:该方法不但有效地获取和表达待识别目标的高层特征知识,而且能有效提高机器人的目标识别与环境感知能力,展现出优越的分类识别性能.

Robot object recognition based on learning and cognition with conceptual space

In view of the challenging issue for robot multi-class object recognition in complex environments with original feature space because those features can only embody some low level knowledge with poor discriminative performance, an approach to robot object recognition was presented based on conceptual space methodology. At first, the basic feature space was built through multi-sensor data fusion and feature extraction, thus gaussian mixture models (GMM) was employed to model objects properties in order to build conceptual space with characteristic of high level knowledge so as to learn the concepts of objects and improve their discriminative performance. Then support vector machine (SVM) was used to carry out multi-class object recognition for robot in indoor clutter environment. The experimental results demonstrate that the proposed method can not only represent the high level knowledge of objects, but also improve the performance of object recognition and environment perception of robot effectively.