Terrain perception in complex environment is important for Autonomous Land Vehicle to drive automatically. In order to access the terrain information, in this paper, we present a terrain perception method based on Hidden Markov Model (HMM) which combines LIDAR with machine vision. On the basis of spatial fan-shaped model, terrain feature extraction is performed to acquire the observation model. Hidden markov models describe the vertical structure of the driving space and Viterbi algorithm is used for terrain classification. Then the navigation decision is given based on the perception of the complex environment. Experiment results show that the method can give an accurate environment description for ALV.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Complex terrain perception based on Hidden Markov Model


    Contributors:
    Wang, Meiling (author) / Zuo, Liang (author) / Yang, Yi (author) / Yang, Qiangrong (author) / Liu, Tong (author)


    Publication date :

    2014-10-01


    Size :

    946893 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Hidden Markov Model-based population synthesis

    Saadi, Ismaïl | Online Contents | 2016


    Fault Diagnosis Method Based on Hidden Markov Model

    Zhang, Wei | Springer Verlag | 2016


    Gait Recognition Based on Embedded Hidden Markov Model

    Zhang, Q. / Xu, S. | British Library Online Contents | 2010


    Learning Profiles Based on Hierarchical Hidden Markov Model

    Galassi, U. / Giordana, A. / Saitta, L. et al. | British Library Conference Proceedings | 2005