Vegetation Detection for Driving in Complex Environments

David Bradley, Ranjith Unnikrishnan, and J. Andrew (Drew) Bagnell
IEEE International Conference on Robotics and Automation, April, 2007.


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Abstract
A key challenge for autonomous navigation in cluttered outdoor environments is the reliable discrimination between obstacles that must be avoided at all costs, and lesser obstacles which the robot can drive over if necessary. Chlorophyll-rich vegetation in particular is often not an obstacle to a capable off-road vehicle, and it has long been recognized in the satellite imaging community that a simple comparison of the red and near-infrared (NIR) reflectance of a material provides a reliable technique for measuring chlorophyll content in natural scenes. This paper evaluates the effectiveness of using this chlorophyll-detection technique to improve autonomous navigation in natural, off-road environments. We demonstrate through extensive experiments that this feature has properties complementary to the color and shape descriptors traditionally used for point cloud analysis, and show significant improvement in classification performance for tasks relevant to outdoor navigation. Results are shown from field testing onboard a robot operating in off-road terrain.

Keywords
mobile robots, vegetation detection, 3D vision

Notes
Sponsor: DARPA (UGCV Perceptor Integrated)
Associated Project(s): UGCV PerceptOR Integrated

Text Reference
David Bradley, Ranjith Unnikrishnan, and J. Andrew (Drew) Bagnell, "Vegetation Detection for Driving in Complex Environments," IEEE International Conference on Robotics and Automation, April, 2007.

BibTeX Reference
@inproceedings{Bradley_2007_5624,
   author = "David Bradley and Ranjith Unnikrishnan and J. Andrew (Drew) Bagnell",
   title = "Vegetation Detection for Driving in Complex Environments",
   booktitle = "IEEE International Conference on Robotics and Automation",
   month = "April",
   year = "2007",
}