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Fast Machine Vision Line Detection for Mobile Robot Navigation in Dark Environments

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Image Processing and Communications Challenges 7

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 389))

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Abstract

Navigation of mobile robots based on video analysis becomes one of the most popular application areas of machine vision in automation and robotics. Recently growing popularity of Unmanned Aerial Vehicles (drones) as well as some other types of autonomous mobile robots leads to rapid increase of their application possibilities e.g. related to exploration of some areas hardly accessible for people, such as caves, underground corridors, bunkers etc. However, such places are specific in view of lighting conditions so many classical image analysis algorithms cannot be applied effectively for navigation of mobile robots in such environments. In order to utilize the image data for robot navigation in such places some modified machine vision algorithms should be applied such as fast line detection based on statistical binarization discussed in this paper.

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Authors and Affiliations

  1. West Pomeranian University of Technology, Szczecin, Poland

    Piotr Lech, Krzysztof Okarma & Jarosław Fastowicz

  2. Faculty of Electrical Engineering, Department of Signal Processing and Multimedia Engineering, 26. Kwietnia 10, 71-126, Szczecin, Poland

    Piotr Lech, Krzysztof Okarma & Jarosław Fastowicz

Authors
  1. Piotr Lech
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  2. Krzysztof Okarma
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  3. Jarosław Fastowicz
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Corresponding author

Correspondence to Piotr Lech .

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Editors and Affiliations

  1. Bydgoszcz, Poland

    Ryszard S. Choraś

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Lech, P., Okarma, K., Fastowicz, J. (2016). Fast Machine Vision Line Detection for Mobile Robot Navigation in Dark Environments. In: Choraś, R. (eds) Image Processing and Communications Challenges 7. Advances in Intelligent Systems and Computing, vol 389. Springer, Cham. https://doi.org/10.1007/978-3-319-23814-2_18

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  • DOI: https://doi.org/10.1007/978-3-319-23814-2_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23813-5

  • Online ISBN: 978-3-319-23814-2

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