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

By Frank Moosmann, Oliver Pink and Christoph Stiller

Abstract

Abstract—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. I

Year: 2009
DOI identifier: 10.1109/ivs.2009.5164280
OAI identifier: oai:CiteSeerX.psu:10.1.1.329.1510
Provided by: CiteSeerX
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