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Statistical and Geometrical Approaches to Visual Motion Analysis pp 209–234Cite as

Combination of Geometrical and Statistical Methods for Visual Navigation of Autonomous Robots

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5604))

Abstract

For visual navigation of an autonomous robot, detection of collision-free direction from an image/ image sequence captured by imaging systems mounted on the robot is a fundamental task. This collision free direction provides the next view to direct attention for computing the next collision free direction. Therefore, the robot requires a cyclic mechanism directing attention to the view and computing the collision free direction from that view. We combine a geometric method for free space detection and a statistical method for visual navigation of the mobile robot. Firstly, we deal with a random-sampling-based method for the detection of free space. Secondly, we deal with a statistical method for the computation of the collision avoiding direction. The robot finds free space using the visual potential defined from a series of views captured by a monocular camera system mounted on the robot to observe the view in front of the robot, We examine the statistical property of the gradient field of the visual potential. We show that the principal component of the gradient of the visual potential field yields the attention direction of the mobile robot for collision avoidance. Some experimental results of navigating the mobile robot in synthetic and real environments are presented.

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Author information

Authors and Affiliations

  1. School of Science and Technology, Chiba University, Japan

    Naoya Ohnishi

  2. Institute of Media and Information Technology, Chiba University, Japan, Yayoicho 1-33, Inage-ku, Chiba, 263-8522, Japan

    Atsushi Imiya

Authors
  1. Naoya Ohnishi
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  2. Atsushi Imiya
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Editor information

Editors and Affiliations

  1. Institut für Informatik III, Universität Bonn, Römerstraße 164, 53117, Bonn, Germany

    Daniel Cremers  & Frank R. Schmidt  & 

  2. Institut für Informationsverarbeitung, Leibniz Universität Hannover, Appelstraße 9A, 30167, Hannover, Germany

    Bodo Rosenhahn

  3. Department of Statistics and Psychology, University of California - Los Angeles, 8967 Math Sciences Building, 90095-1554, Los Angeles, CA, USA

    Alan L. Yuille

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© 2009 Springer-Verlag Berlin Heidelberg

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Ohnishi, N., Imiya, A. (2009). Combination of Geometrical and Statistical Methods for Visual Navigation of Autonomous Robots. In: Cremers, D., Rosenhahn, B., Yuille, A.L., Schmidt, F.R. (eds) Statistical and Geometrical Approaches to Visual Motion Analysis. Lecture Notes in Computer Science, vol 5604. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03061-1_11

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  • DOI: https://doi.org/10.1007/978-3-642-03061-1_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03060-4

  • Online ISBN: 978-3-642-03061-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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