This paper focuses on terrain classification in field environment and proposes a self-supervised terrain classification method which is based on 3D laser sensor and monocular vision sensor to adapt to changes in terrain environment and external conditions. First of all, extract typical traversable areas and typical obstacle areas by analyzing range data from 3D laser sensor and project these two kinds of areas into image space to label the image data. Then extract visual feature from the corresponding image to train classifier to classify the terrain. The experiment results demonstrate that the proposed method in this paper can obtain high classification accuracy and good real-time performance.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Adaptive terrain classification in field environment based on self-supervised learning


    Contributors:
    Dai, Xiaofang (author) / Li, Shulun (author) / Sun, Fengchi (author)


    Publication date :

    2014-08-01


    Size :

    240399 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Supervised Terrain Classification with Adaptive Unsupervised Terrain Assessment

    Kurup, Akhil / Kysar, Sam / Bos, Jeremy et al. | British Library Conference Proceedings | 2021


    Supervised Terrain Classification with Adaptive Unsupervised Terrain Assessment

    Kurup, Akhil / Bos, Jeremy / Jayakumar, Paramsothy et al. | SAE Technical Papers | 2021


    Self-Supervised Classification for Planetary Rover Terrain Sensing

    Brooks, Christopher A. / Iagnemma, Karl D. | IEEE | 2007


    Self-supervised terrain classification for planetary surface exploration rovers

    Brooks, C. A. / Iagnemma, K. | British Library Online Contents | 2012


    Self-supervised learning to visually detect terrain surfaces for autonomous robots operating in forested terrain

    Zhou, S. / Xi, J. / McDaniel, M. W. et al. | British Library Online Contents | 2012