Semantic categorization of indoor places using CNN for mobile robot exploration

The ability of achieving a semantic understanding of workspaces is an important capability for mobile robot. A method is proposed to categorize different places in a typical indoor environment by using a Kinect sensors for mobile robot exploration. At first, the invariant feature based images stitching approach is adopted to form a panoramic image according to Kinect visual information, and the translation between Kinect depth information and obstacle distance information is performed to obtain virtual LIDAR data. Then, the semantic classifier is designed by using convolutional neural networks （CNN） for indoor place eategorization based on Kinect visual observations with panoramic view. At last, a frontier-based exploration method is applied to carry out indoor autonomous exploration of mo- bile robots, which integrates the CNN-based categorization approach. The proposed method has been implemented and tested on a real robot, and experiment results demonstrate the approach effective- ness on solving the semantic categorization problem for mobile robot exploration.

汉语惯用语理解机制研究:来自英文词素的启动效应证据

本文采用跨语言非掩蔽启动条件下的词汇判断实验范式,探究惯用语的表征机制。两个实验分别考查了不同熟悉度英文词素对语义分解度和熟悉度不同的汉语惯用语的启动效应。结果表明,在跨语言条件下,动宾结构惯用语既包含成分表征又包含整词表征,属于混合表征。英文词素对不同性质汉语惯用语的促进作用存在显著差异。语义分解度影响惯用语的表征,语义分解度越高,惯用语越接近一般短语;语义分解度越低,惯用语越接近"整词"。熟悉度影响惯用语的表征,熟悉度越高,惯用语的比喻意义越容易激活。