Single camera vehicle localization using SURF scale and dynamic time warping
Single camera vehicle localization using SURF scale and dynamic time warping
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DOI：
10.1109/ivs.2014.6856545
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发表时间：
2014-06
期刊：
影响因子：
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通讯作者：
D. Wong;Daisuke Deguchi;I. Ide;H. Murase
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文献类型：
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作者：
D. Wong;Daisuke Deguchi;I. Ide;H. Murase
车辆自我定位是许多驾驶员辅助和自动驾驶系统的重要过程。传统的 GPS 定位解决方案在城市环境中通常不可靠，因为城市环境中高层建筑可能会导致卫星信号的遮蔽和多路径传播。典型的基于视觉特征的定位方法依赖于基础矩阵的计算，当基线很小时，基础矩阵可能不稳定。在本文中，我们提出了一种新颖的方法，该方法使用匹配的 SURF 图像特征的尺度和动态时间扭曲来执行稳定的定位。通过比较输入图像和预先构建的数据库之间的 SURF 特征尺度，无需计算基本矩阵即可实现稳定的定位。此外，3D 信息被添加到数据库特征点中，以便执行横向定位，从而进行车道识别。根据从真实交通环境中捕获的实验数据，我们展示了所提出的系统如何能够提供相对于图像数据库的高定位精度，并且还可以执行横向定位以识别车辆当前的车道。
Vehicle ego-localization is an essential process for many driver assistance and autonomous driving systems. The traditional solution of GPS localization is often unreliable in urban environments where tall buildings can cause shadowing of the satellite signal and multipath propagation. Typical visual feature based localization methods rely on calculation of the fundamental matrix which can be unstable when the baseline is small. In this paper we propose a novel method which uses the scale of matched SURF image features and Dynamic Time Warping to perform stable localization. By comparing SURF feature scales between input images and a pre-constructed database, stable localization is achieved without the need to calculate the fundamental matrix. In addition, 3D information is added to the database feature points in order to perform lateral localization, and therefore lane recognition. From experimental data captured from real traffic environments, we show how the proposed system can provide high localization accuracy relative to an image database, and can also perform lateral localization to recognize the vehicle's current lane.