About: Robust detection of shady and highlighted roads for monocular camera based navigation of UGV     Goto   Sponge   NotDistinct   Permalink

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  • This paper addresses the problem of UGV navigation in various environments and lightning conditions. Previous approaches use a combination of different sensors, or work well, only in scenarios with noticeable road marking or borders. Our robot is used for chemical, nuclear and biological contamination measurement. Thus, to avoid complications with decontamination, only a monocular camera serves as a sensor since it is already equipped. In this paper, we propose a novel approach a fusion of frequency based vanishing point estimation and probabilistically based color segmentation. Detection of a vanishing point, is based on the estimation of a texture flow, produced by a bank of Gabor wavelets and a voting function. Next, the vanishing point defines the training area, which is used for self-supervised learning of color models. Finally, road patches are selected by measuring of the roadness score. A few rules deal with dark cast shadows, overexposed highlights and adaptivity speed. In addition to the rob
  • This paper addresses the problem of UGV navigation in various environments and lightning conditions. Previous approaches use a combination of different sensors, or work well, only in scenarios with noticeable road marking or borders. Our robot is used for chemical, nuclear and biological contamination measurement. Thus, to avoid complications with decontamination, only a monocular camera serves as a sensor since it is already equipped. In this paper, we propose a novel approach a fusion of frequency based vanishing point estimation and probabilistically based color segmentation. Detection of a vanishing point, is based on the estimation of a texture flow, produced by a bank of Gabor wavelets and a voting function. Next, the vanishing point defines the training area, which is used for self-supervised learning of color models. Finally, road patches are selected by measuring of the roadness score. A few rules deal with dark cast shadows, overexposed highlights and adaptivity speed. In addition to the rob (en)
Title
  • Robust detection of shady and highlighted roads for monocular camera based navigation of UGV
  • Robust detection of shady and highlighted roads for monocular camera based navigation of UGV (en)
skos:prefLabel
  • Robust detection of shady and highlighted roads for monocular camera based navigation of UGV
  • Robust detection of shady and highlighted roads for monocular camera based navigation of UGV (en)
skos:notation
  • RIV/00216305:26220/11:PU96251!RIV12-MSM-26220___
http://linked.open...avai/predkladatel
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  • Z(MSM0021630529)
http://linked.open...vai/riv/dodaniDat
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  • 227301
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  • RIV/00216305:26220/11:PU96251
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  • Adaptation models , Cameras , Estimation , Image color analysis , Roads , Robots , Training (en)
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http://linked.open...ontrolniKodProRIV
  • [05BCE6F5F4F7]
http://linked.open...v/mistoKonaniAkce
  • Shanghai
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  • Shanghai
http://linked.open...i/riv/nazevZdroje
  • Proceedings of ICRA 2011
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  • Žalud, Luděk
  • Jura, Pavel
  • Mikšík, Ondřej
  • Petyovský, Petr
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
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  • Neuveden
https://schema.org/isbn
  • 978-1-61284-386-5
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  • 26220
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