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International Conference on Intelligent Autonomous Systems

IAS 2018: Intelligent Autonomous Systems 15 pp 689–705Cite as

Concept Study for Vehicle Self-Localization Using Neural Networks for Detection of Pole-Like Landmarks

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

Abstract

This paper discusses and showcases a software framework for the self-localization of autonomous vehicles in an urban environment. The general concept of this framework is based on the semantic detection and observation of objects in the surrounding environment. For the object detection three different perception approaches are compared; LiDAR based, stereo camera based and mono camera based using a neural net. The investigated objects all share the same geometrical shape; they are vertical with a high aspect ratio. To compute the pose of the vehicle an Adaptive Monte-Carlo Algorithm has been implemented. Hence it is necessary to create a high-precision digital map this is done with a dense map, the detected objects and the LiDAR point cloud. Comparison with an earlier paper have shown that this approach keeps the global positioning accuracy around 0.50 m and leads to more robust results in highly dynamic scenarios where a small amount of objects can be detected.

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Author information

Authors and Affiliations

  1. PEM RWTH Aachen, 52074, Aachen, NRW, Germany

    Achim Kampker, Jonas Hatzenbuehler, Lars Klein, Mohsen Sefati, Kai D. Kreiskoether & Denny Gert

Authors
  1. Achim Kampker
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  2. Jonas Hatzenbuehler
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  3. Lars Klein
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  4. Mohsen Sefati
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  5. Kai D. Kreiskoether
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  6. Denny Gert
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Corresponding author

Correspondence to Jonas Hatzenbuehler .

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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Kampker, A., Hatzenbuehler, J., Klein, L., Sefati, M., Kreiskoether, K.D., Gert, D. (2019). Concept Study for Vehicle Self-Localization Using Neural Networks for Detection of Pole-Like Landmarks. 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_54

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