Carnegie Mellon University
Data Structure for Efficient Processing in 3-D

Jean-Francois Lalonde, Nicolas Vandapel, and Martial Hebert
Robotics: Science and Systems 1, July, 2005.

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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.

data structure, classification, 3-D

Sponsor: Army Research Laboratory
Grant ID: DAAD19-01-209912
Associated Center(s) / Consortia: Vision and Autonomous Systems Center and Field Robotics Center
Associated Project(s): CTA Robotics
Number of pages: 8

Text Reference
Jean-Francois Lalonde, Nicolas Vandapel, and Martial Hebert, "Data Structure for Efficient Processing in 3-D," Robotics: Science and Systems 1, July, 2005.

BibTeX Reference
   author = "Jean-Francois Lalonde and Nicolas Vandapel and Martial Hebert",
   title = "Data Structure for Efficient Processing in 3-D",
   booktitle = "Robotics: Science and Systems 1",
   publisher = "MIT Press",
   month = "July",
   year = "2005",