Terrain classification is a fundamental task in outdoor robot navigation to detect and avoid impassable terrain. Camera-based approaches are well-studied and provide good results. A drawback of these approaches, however, is that the quality of the classification varies with the prevailing lighting conditions. 3D laser scanners, on the other hand, are largely illumination-invariant. In this work we present easy to compute features for 3D point clouds using range and intensity values. We compare the classification results obtained using only the laser-based features with the results of camera-based classification and study the influence of different lighting conditions.
@incollection{laible2012ams,
author = {Stefan Laible and Yasir Niaz Khan and Karsten Bohlmann and Andreas
Zell},
title = {3D LIDAR- and Camera-Based Terrain Classification Under Different
Lighting Conditions},
booktitle = {Autonomous Mobile Systems 2012},
publisher = {Springer Berlin Heidelberg},
year = {2012},
series = {Informatik aktuell},
pages = {21--29},
abstract = {Terrain classification is a fundamental task in outdoor robot navigation
to detect and avoid impassable terrain. Camera-based approaches are
well-studied and provide good results. A drawback of these approaches,
however, is that the quality of the classification varies with the
prevailing lighting conditions. 3D laser scanners, on the other hand,
are largely illumination-invariant. In this work we present easy
to compute features for 3D point clouds using range and intensity
values. We compare the classification results obtained using only
the laser-based features with the results of camera-based classification
and study the influence of different lighting conditions.},
affiliation = {Chair of Cognitive Systems, University of T\"ubingen, Department of
Computer Science, Sand 1, 72076 T\"ubingen, Germany},
doi = {10.1007/978-3-642-32217-4_3},
isbn = {978-3-642-32217-4},
keyword = {Engineering},
pdf = {http://www.cogsys.cs.uni-tuebingen.de/publikationen/2012/laible2012ams.pdf},
url = {http://dx.doi.org/10.1007/978-3-642-32217-4_3}
}