• DocumentCode
    3586990
  • Title

    Door recognition and deep learning algorithm for visual based robot navigation

  • Author

    Wei Chen ; Ting Qu ; Yimin Zhou ; Kaijian Weng ; Gang Wang ; Guoqiang Fu

  • Author_Institution
    Fac. of Electromech. Eng., Guangdong Univ. of Technol., Guangzhou, China
  • fYear
    2014
  • Firstpage
    1793
  • Lastpage
    1798
  • 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.
  • Keywords
    SLAM (robots); doors; feature extraction; learning (artificial intelligence); mobile robots; neural nets; object recognition; path planning; pose estimation; robot vision; CNN modelling technique; ambient scene recognition; autonomous robot navigation; biological brain; convolutional neural network modelling technique; deep learning algorithm; door location; door pose prediction; door recognition; feature extraction; indoor environment; mutilayer features; visual based robot navigation; working pattern; Feature extraction; Filter banks; Robots; Satellite navigation systems; Training; Visualization; CNN; Deep Learning; Door Recognition; Indoor Navigation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Robotics and Biomimetics (ROBIO), 2014 IEEE International Conference on
  • Type

    conf

  • DOI
    10.1109/ROBIO.2014.7090595
  • Filename
    7090595