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Context-Aware Recognition of Drivable Terrain with Automated Parameters Estimation

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Intelligent Autonomous Systems 15 (IAS 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 867))

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Abstract

This paper deals with the terrain classification problem for autonomous service robots in semi-structured outdoor environments. The aim is to recognize the drivable terrain in front of a robot that navigates on roads of different surfaces, avoiding areas that are considered non-drivable. Since the system should be robust to such factors as changing lighting conditions, mud and fallen leaves, we employ multi-sensor perception with a monocular camera and a 2D laser scanner. The labeling of the terrain obtained from a Random Trees classifier is refined by context-aware inference using the Conditional Random Field. We demonstrate that automatic learning of the parameters for Conditional Random Fields improves results in comparison to similar approaches without the context-aware inference or with parameters set by hand.

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Authors and Affiliations

  1. Institute of Control, Robotics, and Information Engineering, Poznań University of Technology, ul. Piotrowo 3A, 60-965, Poznań, Poland

    Jan Wietrzykowski & Piotr Skrzypczyński

Authors
  1. Jan Wietrzykowski
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  2. Piotr Skrzypczyński
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Corresponding author

Correspondence to Piotr Skrzypczyński .

Editor information

Editors and Affiliations

  1. Baden-Wuerttemberg Cooperative State University, Karlsruhe, Germany

    Marcus Strand

  2. Humanoids and Intelligence Systems Lab, KIT - Karlsruher Institut für Technologie, Karlsruhe, Germany

    Rüdiger Dillmann

  3. University of Padua , Padua, Italy

    Emanuele Menegatti

  4. University of Padua, Padua, Italy

    Stefano Ghidoni

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Wietrzykowski, J., Skrzypczyński, P. (2019). Context-Aware Recognition of Drivable Terrain with Automated Parameters Estimation. In: Strand, M., Dillmann, R., Menegatti, E., Ghidoni, S. (eds) Intelligent Autonomous Systems 15. IAS 2018. Advances in Intelligent Systems and Computing, vol 867. Springer, Cham. https://doi.org/10.1007/978-3-030-01370-7_49

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