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  • Fusion of Sequential Information forSemantic Grid Map Estimation

    Frank Bieder, Muti Ur Rehman, Christoph Stiller

    Kapitel/Beitrag aus dem Buch: Heizmann M. & Längle T. 2020. Forum Bildverarbeitung 2020.

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    In this work, we improve the semantic segmentation
    of multi-layer top-view grid maps in the context of LiDARbased
    perception for autonomous vehicles. To achieve this
    goal, we fuse sequential information from multiple consecutive
    lidar measurements with respect to the driven trajectory
    of an autonomous vehicle. By doing so, we enrich the multilayer
    grid maps which are subsequently used as the input of
    a neural network. Our approach can be used for LiDAR-only
    360 surround view semantic scene segmentation while being
    suitable for real-time critical systems. We evaluate the benefit
    of fusing sequential information based on a dense ground
    truth and discuss the effect on different semantic classes.

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    Empfohlene Zitierweise für das Kapitel/den Beitrag
    Bieder, F et al. 2020. Fusion of Sequential Information forSemantic Grid Map Estimation. In: Heizmann M. & Längle T (eds.), Forum Bildverarbeitung 2020. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.58895/ksp/1000124383-7
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    This chapter distributed under the terms of the Creative Commons Attribution + ShareAlike 4.0 license. Copyright is retained by the author(s)

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    Veröffentlicht am 25. November 2020

    DOI
    https://doi.org/10.58895/ksp/1000124383-7