Submitted by bradski on Thu, 09/16/2010 - 19:13
| Title | Self-supervised Monocular Road Detection in Desert Terrain |
| Publication Type | Conference Paper |
| Year of Publication | 2006 |
| Authors | Dahlkamp, H.., Bradski, Gary., Kaehler, Adrian., Stavens, D.., and Thrun, Sebastian |
| Conference Name | RSS |
| Date Published | 2006 |
| Conference Location | Philadelphia |
| Abstract | We present a method for identifying drivable surfaces in difficult unpaved and offroad terrain conditions as
encountered in the DARPA Grand Challenge robot race. Instead
of relying on a static, pre-computed road appearance model, this
method adjusts its model to changing environments. It achieves
robustness by combining sensor information from a laser range
finder, a pose estimation system and a color camera. Using the
first two modalities, the system first identifies a nearby patch
of drivable surface. Computer Vision then takes this patch and
uses it to construct appearance models to find drivable surface
outward into the far range. This information is put into a
drivability map for the vehicle path planner. In addition to
evaluating the method’s performance using a scoring framework
run on real-world data, the system was entered, and won, the
2005 DARPA Grand Challenge. Post-race log-file analysis proved
that without the Computer Vision algorithm, the vehicle would
not have driven fast enough to win.
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