• DocumentCode
    3108204
  • Title

    A Robust Obstacle Detection Method in Highly Textured Environments Using Stereo Vision

  • Author

    Fazli, Saeid ; Dehnavi, Hajar Mohammadi ; Moallem, Payman

  • Author_Institution
    Electr. Dept., Zanjan Univ., Zanjan, Iran
  • fYear
    2009
  • fDate
    28-30 Dec. 2009
  • Firstpage
    97
  • Lastpage
    100
  • Abstract
    Stereo vision based obstacle detection is an algorithm that aims to detect and compute obstacle depth using stereo matching and disparity map. This paper presents a robust method to detect positive obstacles including staircases in highly textured environments. The proposed method is easy to implement and fast enough for obstacle avoidance. This work is partly inspired by the work of Nicholas Molton et al. The algorithm consists of several steps including calibration, pre processing, obstacle detection, analysis of disparity map and depth computation. This method works well in highly textured environments and ideal for real applications. An adaptive thresholding is also applied for better noise and texture removal. Experimental results show the effectiveness of the proposed method.
  • Keywords
    adaptive signal processing; calibration; computer vision; image denoising; image matching; image segmentation; image texture; object detection; stereo image processing; adaptive thresholding; calibration step; depth computation; disparity map analysis; noise removal; obstacle avoidance; preprocessing step; robust obstacle detection method; stereo matching; stereo vision; texture removal; Application software; Calibration; Cameras; Computer vision; Image reconstruction; Layout; Navigation; Robustness; Stereo image processing; Stereo vision; highly textured environments; obstacle detection; positive obstacle; staircase; stereo vision;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Vision, 2009. ICMV '09. Second International Conference on
  • Conference_Location
    Dubai
  • Print_ISBN
    978-0-7695-3944-7
  • Electronic_ISBN
    978-1-4244-5645-1
  • Type

    conf

  • DOI
    10.1109/ICMV.2009.48
  • Filename
    5381092