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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    Title :

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


    Contributors:


    Publication date :

    2009-06-01


    Size :

    3080046 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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