terrain classification techniques from ladar data for autonomous navigation

Martial Hebert and Nicolas Vandapel
Collaborative Technology Alliances conference, May, 2003.


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Abstract
Autonomous navigation remains a considerable challenge, primarily because of the difficulty in describing the environment of the robot in a way that captures the variability of natural environments. In this paper, we focus on the problem of extracting the ground terrain surface from sparse 3-D data from LADAR mobility sensors, including the segmentation of the terrain from obscuring vegetation. In this paper, we briefly review possible approaches to LADAR processing, discuss their limitations, and describe our current approach. Results obtained with the GDRS CTA LADAR are presented

Keywords
laser, vegetation, terrain classification, obstacle detection

Notes
Sponsor: U.S. Army Research Laboratory
Associated Center(s) / Consortia: Vision and Autonomous Systems Center and Field Robotics Center
Associated Project(s): CTA Robotics

Text Reference
Martial Hebert and Nicolas Vandapel, "terrain classification techniques from ladar data for autonomous navigation," Collaborative Technology Alliances conference, May, 2003.

BibTeX Reference
@inproceedings{Hebert_2003_4416,
   author = "Martial Hebert and Nicolas Vandapel",
   title = "terrain classification techniques from ladar data for autonomous navigation",
   booktitle = "Collaborative Technology Alliances conference",
   month = "May",
   year = "2003",
}