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

Forrest N. Bush, J. Esposito
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引用次数: 7

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.
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基于视觉的自动地面车辆车道检测:比较现场测试
我们研究了设计计算机视觉算法来自动驾驶越野车在两个车道标记之间的问题。在本文中,我们描述了用于比较文献中两种流行的线提取技术的有效性的现场测试:霍夫变换和RANSAC算法。虽然它非常依赖于实现,但我们发现Hough变换在非道路环境中识别车道标记的速度和准确性方面都优于RANSAC算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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