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Lane detection localization for UGV in urban environment

机译:城市环境中UGV的车道检测与本地化

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Generally, the main components of autonomous driving system consists of perception (geometry recognition, localization, and objects detection & tracking) and navigation processes (global & local path planning, and controller). In this paper, we focus on finding an accurate position for Unmanned Ground Vehicle (UGV) in urban environments. A GPS sensor is fundamentally used to get a current global position, but its accuracy is susceptible to satellite geometry or receiver's conditions; even though high performance guaranteed GPS such as differential-GPS (DGPS) always return precise position(position error is less than 0.4m) because of problems such as disconnection between base station and vehicle. The position-errors, even few meters, could engender serious accidents when UGV is driving under urban environments. In this paper, we suggest the localization algorithm and novel lane detection algorithm. The detected lane information is implemented to overcome GPS sensor's position-errors. First, our novel lane detection algorithm is described, and then the localization algorithm is discussed. This paper also provides experimental results of the lane-detection and the localization in urban environment by using UGV.
机译:通常,自主驱动系统的主要组件包括感知(几何识别,定位和对象检测和跟踪)和导航过程(全局和本地路径规划和控制器)。在本文中,我们专注于在城市环境中寻找无人面的地面车辆(UGV)的准确立场。 GPS传感器基本上用于获得当前的全球位置,但其准确性易于卫星几何形状或接收器的条件;尽管高性能保证GPS等差分GPS(DGPS)始终返回精确位置(位置误差小于0.4米),因为基站和车辆之间的断开等问题。当UGV在城市环境下驾驶时,甚至几米的位置误差可能会发生严重的事故。在本文中,我们建议本地化算法和新型车道检测算法。实现了检测到的车道信息以克服GPS传感器的位置误差。首先,描述了我们的新型车道检测算法,然后讨论了定位算法。本文还通过使用UGV提供了路线检测和城市环境中的本地化的实验结果。

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