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Technical Program
Paper Detail
| Paper: | WP-P1.9 |
| Session: | Visual Object/Event Detection, Segmentation, and Classification |
| Time: | Wednesday, October 11, 14:20 - 17:00 |
| Presentation: |
Poster
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| Title: |
DETECTION OF DRIVABLE CORRIDORS FOR OFF-ROAD AUTONOMOUS NAVIGATION |
| Authors: |
Ara Nefian; Intel | | | | | Gary Bradski; Intel | | |
| Abstract: |
This paper describes a hierarchical Bayesian network used for segmenting desert images and detecting off road drivable corridors for autonomous navigation. Unlike the embedded hidden Markov model the Bayesian network presented in this paper can successfully account for natural dependencies between neighboring pixels in both image dimensions making it more suitable for a larger class of images. The method described here was developed within the Stanford racing team that won the DARPA Grand Challenge 2005 after driving over 130 miles autonomously in the Nevada desert. |
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