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

fYear

2006

Firstpage

3025

Lastpage

3028

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 2006 IEEE International Conference on

Conference_Location

Atlanta, GA

ISSN

1522-4880

Print_ISBN

1-4244-0480-0

Type

conf

DOI

10.1109/ICIP.2006.313004

Filename

4107207