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Natural Terrain Classification using 3-D Ladar Data

Nicolas Vandapel, Daniel Huber, Anuj Kapuria and Martial Hebert
Conference Paper, Carnegie Mellon University, IEEE International Conference on Robotics and Automation, Vol. 5, pp. 5117 - 5122, April, 2004

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Abstract

Because of the difficulty of interpreting laser data in a meaningful way, safe navigation in vegetated terrain is still a daunting challenge. In this paper, we focus on the segmentation of ladar data using local 3-D point statistics into three classes: clutter to capture grass and tree canopy, linear to capture thin objects like wires or tree branches, and finally surface to capture solid objects like ground terrain surface, rocks or tree trunks. We present the details of the method proposed, the modifications we made to implement it on-board an autonomous ground vehicle. Finally, we present results from field tests using this rover and results produced from different stationary laser sensors.

BibTeX Reference
@conference{Vandapel-2004-8893,
title = {Natural Terrain Classification using 3-D Ladar Data},
author = {Nicolas Vandapel and Daniel Huber and Anuj Kapuria and Martial Hebert},
booktitle = {IEEE International Conference on Robotics and Automation},
sponsor = {U. S. Army Research Laboratory},
grantID = {DAAD19-01-2-0012},
school = {Robotics Institute , Carnegie Mellon University},
month = {April},
year = {2004},
volume = {5},
pages = {5117 - 5122},
address = {Pittsburgh, PA},
}
2017-09-13T10:44:06+00:00