Paper Info
Title
Door recognition and deep learning algorithm for visual based robot navigation
Abstract
In this paper, a new method based on deep learning for robotics autonomous navigation is presented. Different from the most traditional methods based on fixed models, a convolutional neural network (CNN) modelling technique in Deep learning is selected to extract the feature inspired by the working pattern of the biological brain. This neural network model has muti-layer features where the ambient scenes can be recognized and useful information such as the location of door can be identified. The extracted information can be used for robot navigation, so does the robot can approach the target accurately. In the field experiments, detecting doors and predicting the door poses such tasks are designed in the indoor environment to verify the proposed method. The experimental results demonstrate that the doors can be identified with good performance and the deep learning model is suitable for robot navigation.
Year
DOI
Venue
2014
10.1109/ROBIO.2014.7090595
ROBIO
Keywords
Field
DocType
indoor navigation,convolutional neural network modelling technique,working pattern,learning (artificial intelligence),ambient scene recognition,doors,mobile robots,deep learning,mutilayer features,biological brain,pose estimation,cnn,feature extraction,autonomous robot navigation,path planning,visual based robot navigation,slam (robots),object recognition,cnn modelling technique,door location,indoor environment,door pose prediction,door recognition,neural nets,deep learning algorithm,robot vision,visualization,robots
Robot learning,Computer vision,Convolutional neural network,Feature extraction,Artificial intelligence,Engineering,Mobile robot navigation,Deep learning,Artificial neural network,Robot,Mobile robot,Machine learning
Conference
Citations 
PageRank 
References 
8
0.50
12
Authors
6
Name
Order
Citations
PageRank
Wei Chen1513.02
Ting Qu2192.52
Yimin Zhou3236.52
Kaijian Weng4161.29
Gang Wang528265.93
Guoqiang Fu63029.70