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Panorama-Based Multilane Recognition for Advanced Navigation Map Generation

机译：基于全景图的多车道识别，用于高级导航地图生成

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摘要

Precise navigation map is crucial in many fields. This paper proposes a panorama based method to detect and recognize lane markings and traffic signs on the road surface. Firstly, to deal with the limited field of view and the occlusion problem, this paper designs a vision-based sensing systemwhich consists of a surround view system and a panoramic system. Secondly, in order to detect and identify traffic signs on the road surface, sliding window based detection method is proposed. Template matching method and SVM (Support Vector Machine) are used to recognize the traffic signs. Thirdly, to avoid the occlusion problem, this paper utilities vision based ego-motion estimation to detect and remove other vehicles. As surround view images contain less dynamic information and gray scales, improved ICP (Iterative Closest Point) algorithm is introduced to ensure that the ego-motion parameters are consequently obtained. For panoramic images, optical flow algorithm is used. The results from the surround view system help to filter the optical flow and optimize the ego-motion parameters; other vehicles are detected by the optical flow feature. Experimental results show that it can handle different kinds of lane markings and traffic signs well.
机译：精确的导航图在许多领域都至关重要。本文提出了一种基于全景的方法来检测和识别路面上的车道标记和交通标志。首先，为了解决有限的视野和遮挡问题，本文设计了一种基于视觉的传感系统，该系统由环视系统和全景系统组成。其次，为了检测和识别路面上的交通标志，提出了一种基于滑动窗口的检测方法。模板匹配方法和SVM（支持向量机）用于识别交通标志。第三，为了避免遮挡问题，本文利用基于视觉的自我运动估计来检测和去除其他车辆。由于周围的视图图像包含较少的动态信息和灰度，因此引入了改进的ICP（迭代最近点）算法，以确保因此获得自我运动参数。对于全景图像，使用光流算法。环视系统的结果有助于过滤光流并优化自我运动参数；其他车辆通过光流特征检测。实验结果表明，它能够很好地处理各种车道标志和交通标志。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第5期|713753.1-713753.14|共14页
  • 作者

    Yang Ming; Gu Xiaolin; Lu Hao; Wang Chunxiang; Ye Lei;

  • 作者单位

    Minist Educ China, Key Lab Syst Control & Informat Proc, Shanghai 200240, Peoples R China.;

    Minist Educ China, Key Lab Syst Control & Informat Proc, Shanghai 200240, Peoples R China.;

    Minist Educ China, Key Lab Syst Control & Informat Proc, Shanghai 200240, Peoples R China.;

    Shanghai Jiao Tong Univ, Res Inst Robot, Shanghai 200240, Peoples R China.;

    Natl Univ Def Technol, Coll Mechatron Engn & Automat, Changsha 410073, Hunan, Peoples R China.;

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