• DocumentCode
    1800334
  • Title

    Terrain typing for real robots

  • Author

    Davis, Ian Lane ; Kelly, Alonzo ; Stentz, Anthony ; Matthies, Larry

  • Author_Institution
    Robotics Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA
  • fYear
    1995
  • fDate
    25-26 Sep 1995
  • Firstpage
    400
  • Lastpage
    405
  • Abstract
    Many robotics tasks require an ability to determine quickly the nature of the terrain surrounding the robot. While much attention has been given to the general problem of terrain typing, the problem of effective real-time terrain typing remains open. For robot missions such as construction site work, military reconnaissance, hazardous waste removal, and planetary exploration this problem must be addressed. In particular, for cross country navigation with a wheeled vehicle, the robot needs to know where the vegetation is and where the rigid obstacles are because frequently the optimal, if not the only, path will pass through vegetation. Our groups have independently researched the problem of finding vegetation in a scene, and have developed systems tuned to the specific demands of real-time terrain typing for robots. This paper looks at three classifiers of increasing dimensionality and describes their applicability to different aspects of the terrain typing problem
  • Keywords
    image classification; image colour analysis; navigation; neural nets; object recognition; real-time systems; robot vision; colour images; cross country navigation; image classifiers; neural networks; real-time systems; robot vision; terrain typing; vegetation; wheeled vehicle; Aircraft navigation; Hidden Markov models; Laboratories; Mobile robots; Pixel; Propulsion; Robot kinematics; Roentgenium; Vegetation mapping; Vehicles;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Vehicles '95 Symposium., Proceedings of the
  • Conference_Location
    Detroit, MI
  • Print_ISBN
    0-7803-2983-X
  • Type

    conf

  • DOI
    10.1109/IVS.1995.528315
  • Filename
    528315