Present object detection methods working on 3D range data are so far either optimized for unstructured offroad environments or flat urban environments. We present a fast algorithm able to deal with tremendous amounts of 3D Lidar measurements. It uses a graph-based approach to segment ground and objects from 3D lidar scans using a novel unified, generic criterion based on local convexity measures. Experiments show good results in urban environments including smoothly bended road surfaces.


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    Titel :

    Segmentation of 3D lidar data in non-flat urban environments using a local convexity criterion


    Beteiligte:
    Moosmann, Frank (Autor:in) / Pink, Oliver (Autor:in) / Stiller, Christoph (Autor:in)


    Erscheinungsdatum :

    2009-06-01


    Format / Umfang :

    3080046 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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