• DocumentCode
    1997693
  • Title

    Binocular Vision Based Drivable Region Fast Detection for Indoor Mobile Robot

  • Author

    Qu Shengyue ; Meng Cai

  • Author_Institution
    Image Process. Center, Beihang Univ., Beijing, China
  • fYear
    2013
  • fDate
    3-4 Dec. 2013
  • Firstpage
    257
  • Lastpage
    261
  • Abstract
    A fast method based on binocular vision is proposed for mobile robot to detect drivable regions. First, the image is segmented into regions by searching contours. Second, part obstacle regions are determined by the vanishing line. Then, according to the different distribution of feature points extracted from the regions under the vanishing line, we use two different method to classify regions: various constraints-based region classification is used to classify regions including many feature points and homography-substraction-based region classification is used to classify regions including rare feature points. Finally, combining the two classification methods, we get the result of drivable region detection. The results of indoor and outdoor experiments show that the method can detect drivable regions quickly and robustly.
  • Keywords
    feature extraction; image classification; image matching; image segmentation; mobile robots; object detection; path planning; probability; robot vision; binocular vision based drivable region fast detection; drivable region probabilities; feature classification; feature correspondence matching; feature point extraction; image segmentation; indoor experiments; indoor mobile robot; outdoor experiments; Accuracy; Cameras; Feature extraction; Gray-scale; Image segmentation; Mobile robots; Robustness; binocular vision; drivable region detection; feature classification; image segmentation; mobile robot;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Systems (GCIS), 2013 Fourth Global Congress on
  • Conference_Location
    Hong Kong
  • Print_ISBN
    978-1-4799-2885-9
  • Type

    conf

  • DOI
    10.1109/GCIS.2013.47
  • Filename
    6805944