NDLI: Fast segmentation of 3D point clouds for ground vehicles

Content Provider	IEEE Xplore Digital Library
Author	Himmelsbach, M. Hundelshausen, F.v. Wuensche, H.
Copyright Year	2010
Description	Author affiliation: Department of Aerospace Engineering, Autonomous Systems Technology (TAS), University of the Bundeswehr Munich, Neubiberg, Germany (Himmelsbach, M.; Hundelshausen, F.v.; Wuensche, H.)
Abstract	This paper describes a fast method for segmentation of large-size long-range 3D point clouds that especially lends itself for later classification of objects. Our approach is targeted at high-speed autonomous ground robot mobility, so real-time performance of the segmentation method plays a critical role. This is especially true as segmentation is considered only a necessary preliminary for the more important task of object classification that is itself computationally very demanding. Efficiency is achieved in our approach by splitting the segmentation problem into two simpler subproblems of lower complexity: local ground plane estimation followed by fast 2D connected components labeling. The method's performance is evaluated on real data acquired in different outdoor scenes, and the results are compared to those of existing methods. We show that our method requires less runtime while at the same time yielding segmentation results that are better suited for later classification of the identified objects.
Starting Page	560
Ending Page	565
File Size	2219030
Page Count	6
File Format	PDF
ISBN	9781424478668
ISSN	19310587
e-ISBN	9781424478682
DOI	10.1109/IVS.2010.5548059
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-21
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clouds Land vehicles Object detection Laser radar Mobile robots Layout Real time systems Remotely operated vehicles Sensor phenomena and characterization Robot sensing systems
Content Type	Text
Resource Type	Article

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