Monocular visual odometry with road probability distribution factor for lane-level vehicle localization

2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV) Pub Date : 2016-11-01 DOI:10.1109/ICARCV.2016.7838777

D. Salleh, E. Seignez

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引用次数: 3

Abstract

Towards achieving lane-level localization, precision and accuracy plays an important role in vehicle localization efficiency. While Global Positioning System (GPS) is usually used for localization, it has low accuracy caused by signal degradation due to several reasons such as lack of well-positioned satellites, signal obstruction or multipath error. Thus, multi-sensor data fusion has been widely studied to improve vehicle localization. By utilizing the existing techniques for monocular visual odometry and particle filter localization, this paper presents how road information available in OpenStreetMap contributes to accurate and precise vehicle localization by exploiting road probability distribution factor in particle filter implementation. This approach was verified in two datasets with different road features and it has shown better performance compared with the established particle filter localization. As our results indicate, this approach is feasible for lane-level localization for intelligent vehicles.

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基于道路概率分布因子的单目视觉里程法车道级车辆定位

为了实现车道级定位，精度和准确度对车辆定位效率起着至关重要的作用。全球定位系统(GPS)通常用于定位，但由于缺乏定位良好的卫星、信号阻碍或多径误差等原因导致信号退化，导致定位精度较低。因此，多传感器数据融合被广泛研究，以提高车辆定位。本文利用现有的单目视觉里程计和粒子滤波定位技术，介绍了OpenStreetMap中可用的道路信息如何在粒子滤波实现中利用道路概率分布因子来实现准确和精确的车辆定位。在两个具有不同道路特征的数据集上进行了验证，结果表明该方法比所建立的粒子滤波定位方法具有更好的性能。结果表明，该方法对智能车辆的车道级定位是可行的。

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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2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV)

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