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Traversable Ground Surface Segmentation and Modeling for Real-Time Mobile Mapping

Wei Song,Seoungjae Cho,Kyungeun Cho,Kyhyun Um,C. Won,Sungdae Sim

Published 2014 in Int. J. Distributed Sens. Networks

ABSTRACT

Remote vehicle operator must quickly decide on the motion and path. Thus, rapid and intuitive feedback of the real environment is vital for effective control. This paper presents a real-time traversable ground surface segmentation and intuitive representation system for remote operation of mobile robot. Firstly, a terrain model using voxel-based flag map is proposed for incrementally registering large-scale point clouds in real time. Subsequently, a ground segmentation method with Gibbs-Markov random field (Gibbs-MRF) model is applied to detect ground data in the reconstructed terrain. Finally, we generate a texture mesh for ground surface representation by mapping the triangles in the terrain mesh onto the captured video images. To speed up the computation, we program a graphics processing unit (GPU) to implement the proposed system for large-scale datasets in parallel. Our proposed methods were tested in an outdoor environment. The results show that ground data is segmented effectively and the ground surface is represented intuitively.

PUBLICATION RECORD

  • Publication year

    2014

  • Venue

    Int. J. Distributed Sens. Networks

  • Publication date

    2014-04-01

  • Fields of study

    Computer Science, Engineering, Environmental Science

  • Identifiers
    DOI 10.1155/2014/795851
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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