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Data Structure for Efficient Processing in 3-D

Jean-Francois Lalonde, Nicolas Vandapel and Martial Hebert
Conference Paper, Carnegie Mellon University, Robotics: Science and Systems 1, June, 2005

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Abstract

Autonomous navigation in natural environment requires three-dimensional (3-D) scene representation and interpretation. High density laser-based sensing is commonly used to capture the geometry of the scene, producing large amount of 3-D points with variable spatial density. We proposed a terrain classification method using such data. The approach relies on the computation of local features in 3-D using a support volume and belongs, as such, to a larger class of computational problems where range searches are necessary. This operation on traditional data structure is very expensive and, in this paper, we present an approach to address this issue. The method relies on reusing already computed data as the terrain classification process progresses over the environment representation. We present results that show significant speed improvement using ladar data collected in various environments with a ground mobile robot.

BibTeX Reference
@conference{Lalonde-2005-9199,
title = {Data Structure for Efficient Processing in 3-D},
author = {Jean-Francois Lalonde and Nicolas Vandapel and Martial Hebert},
booktitle = {Robotics: Science and Systems 1},
keyword = {data structure, classification, 3-D},
sponsor = {Army Research Laboratory},
publisher = {MIT Press},
grantID = {DAAD19-01-209912},
school = {Robotics Institute , Carnegie Mellon University},
month = {June},
year = {2005},
address = {Pittsburgh, PA},
}
2017-09-13T10:43:24+00:00