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Abstract

In recent years, much progress has been made in outdoor autonomous navigation. However, safe navigation is still a daunting challenge in terrain containing vegetation. In this paper, we focus on the segmentation of ladar data into three classes using local three-dimensional point cloud statistics. The classes are: scatter to represent porous volumes such as grass and tree canopy;linear to capture thin objects like wires or tree branches, and finally surface to capture solid objects like ground surface, rocks, or large trunks. We present the details of the proposed method, and the modifications we made to implement it on-board an autonomous ground vehicle for real-time data processing. Finally,we present results produced from different stationary laser sensors and from field tests using an unmanned ground vehicle

Notes

Sponsor: Army Research Laboratory

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Text Reference

Jean-Francois Lalonde, Nicolas Vandapel, Daniel Huber, and Martial Hebert, "Natural terrain classification using three-dimensional ladar data for ground robot mobility," Journal of Field Robotics, Vol. 23, No. 10, November, 2006, pp. 839 - 861.

BibTeX Reference

@article{Lalonde_2006_5611,
   author = "Jean-Francois Lalonde and Nicolas Vandapel and Daniel Huber and Martial Hebert",
   title = "Natural terrain classification using three-dimensional ladar data for ground robot mobility",
   journal = "Journal of Field Robotics",
   pages = "839 - 861",
   publisher = "Wiley",
   month = "November",
   year = "2006",
   volume = "23",
   number = "10",
}

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