• DocumentCode
    2960418
  • Title

    Object recognition in 3D lidar data with recurrent neural network

  • Author

    Prokhorov, Danil V.

  • Author_Institution
    TTC-TEMA, Toyota Res. Inst. NA, Ann Arbor, MI, USA
  • fYear
    2009
  • fDate
    20-25 June 2009
  • Firstpage
    9
  • Lastpage
    15
  • Abstract
    This paper introduces a new method for object recognition which is based on a recurrent neural network trained in a supervised mode. The RNN inputs 3-dimensional laser scanner data sequentially, in a natural temporal order in which the laser returns arrive to the scanner. The method is illustrated on a two-class problem with real data.
  • Keywords
    learning (artificial intelligence); object recognition; optical radar; recurrent neural nets; 3D lidar data; object recognition; recurrent neural network; supervised learning; Cameras; Clouds; Distance measurement; Hardware; Laser modes; Laser radar; Object recognition; Recurrent neural networks; Remotely operated vehicles; Turning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009. IEEE Computer Society Conference on
  • Conference_Location
    Miami, FL
  • ISSN
    2160-7508
  • Print_ISBN
    978-1-4244-3994-2
  • Type

    conf

  • DOI
    10.1109/CVPRW.2009.5204114
  • Filename
    5204114
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2960418