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 sensor...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.展开更多
This study intends to investigate the effects of receptive vocabulary breadth and depth on listening comprehension by college students. The experimental results indicate that both the breadth and depth have a signific...This study intends to investigate the effects of receptive vocabulary breadth and depth on listening comprehension by college students. The experimental results indicate that both the breadth and depth have a significant correlation with listening comprehension; vocabulary breadth contributes more to EFL learners＇ listening comprehension. The findings of this study give pedagogical implications for the teaching of listening in China.展开更多
基金Supported by the National Key Basic Research Program of China(No.2013CB035503)
文摘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.
基金funded by the Philosophy and Social Science Foundation of Yunnan Province(No.YB2013072)
文摘This study intends to investigate the effects of receptive vocabulary breadth and depth on listening comprehension by college students. The experimental results indicate that both the breadth and depth have a significant correlation with listening comprehension; vocabulary breadth contributes more to EFL learners＇ listening comprehension. The findings of this study give pedagogical implications for the teaching of listening in China.
