Title :
Detection of Drivable Corridors for Off-Road Autonomous Navigation
Author :
Nefian, A.V. ; Bradski, G.R.
Author_Institution :
Appl. Res. Lab., Intel Corp., Santa Clara, CA, USA
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.
Keywords :
automated highways; belief networks; image segmentation; mobile robots; navigation; object detection; path planning; DARPA Grand Challenge 2005; Nevada desert; Stanford racing team; desert image segmentation; embedded hidden Markov model; hierarchical Bayesian network; mobile robot motion-planning; off road drivable corridors detection; off-road autonomous navigation; Bayesian methods; Hidden Markov models; Image segmentation; Laser modes; Mobile robots; Navigation; Pixel; Power lasers; Roads; Shape; Mobile robot motion-planning; hidden Markov models; image segmentation;
Conference_Titel :
Image Processing, 2006 IEEE International Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
1-4244-0480-0
DOI :
10.1109/ICIP.2006.313004
