Vision-based lane detection for an autonomous ground vehicle: A comparative field test

Author

Bush, Forrest N. ; Esposito, Joel M.

Author_Institution

US Naval Acad., Annapolis, MD, USA

fYear

2010

fDate

7-9 March 2010

Firstpage

35

Lastpage

39

Abstract

We examine the problem of designing computer vision algorithms to autonomously drive an off road vehicle between two lane markings painted on the ground. In this paper we describe field tests used to compare the efficacy of two popular line extractions techniques from the literature: the Hough Transform and the RANSAC Algorithm. Although it is very implementation dependent, we found the Hough Transform to be superior to the RANSAC algorithm in both speed and accuracy for identifying lane markings in the off road environment.

Keywords

Hough transforms; image sensors; mobile robots; remotely operated vehicles; robot vision; Hough transform; RANSAC algorithm; autonomous ground vehicle; comparative field test; computer vision algorithms design; line extractions techniques; off road vehicle; vision-based lane detection; Computer vision; Global Positioning System; Intelligent vehicles; Land vehicles; Mobile robots; Remotely operated vehicles; Road transportation; Road vehicles; Testing; Vehicle detection; Unmanned Vehicles; Vision-Based Control;

fLanguage

English

Publisher

ieee

Conference_Titel

System Theory (SSST), 2010 42nd Southeastern Symposium on

Conference_Location

Tyler, TX

ISSN

0094-2898

Print_ISBN

978-1-4244-5690-1

Type

conf

DOI

10.1109/SSST.2010.5442799

Filename

5442799