A Pipeline for the Segmentation and Classification of 3D Point Clouds. Douillard, B., Underwood, J., Vlaskine, V., Quadros, A., & Singh, S. P. N. In Experimental Robotics: The 12th International Symposium on Experimental Robotics. Springer, 2010. Paper abstract bibtex This paper presents algorithms for fast segmentation of 3D point clouds and subsequent classification of the obtained 3D segments. The method jointly determines the ground surface and segments individual objects in 3D, including overhanging structures. When compared to six other terrain modelling techniques, this approach has minimal error between the sensed data and the representation; and is fast (processing a Velodyne scan in approximately 2 seconds). Applications include improved alignment of successive scans by enabling operations in sections (Velodyne scans are aligned 7% sharper compared to an approach using raw points) and more informed decision-making (paths move around overhangs). The use of segmentation to aid classification through 3D features, such as the Spin Image or the Spherical Harmonic Descriptor, is discussed and experimentally compared. Moreover, the segmentation facilitates a novel approach to 3D classification that bypasses feature extraction and directly compares 3D shapes via the ICP algorithm. This technique is shown to achieve accuracy on par with the best feature based classifier (92.1%) while being significantly faster and allowing a clearer understanding of the classifier's behaviour.
@INCOLLECTION{iser.2010.3dclassification,
author = {B. Douillard and J. Underwood and V. Vlaskine and A. Quadros and
S. P. N. Singh},
title = {A Pipeline for the Segmentation and Classification of {3D} Point
Clouds},
booktitle = {Experimental Robotics: The 12th International Symposium on Experimental
Robotics},
publisher = {Springer},
year = {2010},
abstract = {This paper presents algorithms for fast segmentation of 3D point clouds
and subsequent classification of the obtained 3D segments. The method
jointly determines the ground surface and segments individual objects
in 3D, including overhanging structures. When compared to six other
terrain modelling techniques, this approach has minimal error between
the sensed data and the representation; and is fast (processing a
Velodyne scan in approximately 2 seconds). Applications include improved
alignment of successive scans by enabling operations in sections
(Velodyne scans are aligned 7% sharper compared to an approach using
raw points) and more informed decision-making (paths move around
overhangs). The use of segmentation to aid classification through
3D features, such as the Spin Image or the Spherical Harmonic Descriptor,
is discussed and experimentally compared. Moreover, the segmentation
facilitates a novel approach to 3D classification that bypasses feature
extraction and directly compares 3D shapes via the ICP algorithm.
This technique is shown to achieve accuracy on par with the best
feature based classifier (92.1%) while being significantly faster
and allowing a clearer understanding of the classifier's behaviour.},
pdf = {iser.2010.3dclassification.pdf},
url = {http://iser2010.grasp.upenn.edu/sites/iser2010/files/papers/ISER2010_0055_1987d92478e380a648d2ffe1090dc390.pdf}
}
Downloads: 0
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