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Vehicle Positioning in Road Networks without GPS

机译:没有GPS的道路网中的车辆定位

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Estimating vehicle position on road maps is important for many ITS applications. Advanced Driver Assistance System (ADAS) may expect robust positioning invariant to day time, weather or simplifications induced by the topological representations of road maps. This paper describes a particle filter approach used to achieve vehicle positioning on freely available road maps. Anti-lock Braking System (ABS) and Electronic Stability Program (ESP) sensor data are used in the motion update model. Measurement updates solely rely on vehicle heading. As our results indicate, our approach is able to cope with the odometry error accumulation. We also found that our methodology is able to successfully localize and track a vehicle with a median error of 3.6m in a road network made of 380km of drivable roads with a performance comparable to a high-end INS unit.
机译:在许多ITS应用中,估计路线图上的车辆位置很重要。先进的驾驶员辅助系统(ADAS)可能会希望稳健的定位不会因白天的时间,天气或路线图的拓扑表示而简化。本文介绍了一种粒子过滤器方法,该方法用于在免费路线图上实现车辆定位。运动更新模型中使用了防抱死制动系统(ABS)和电子稳定程序(ESP)传感器数据。测量更新仅取决于车辆的前进方向。如我们的结果所示,我们的方法能够应对里程表误差累积。我们还发现,我们的方法能够成功定位和跟踪由380公里可行驶道路组成的路网中的平均误差为3.6m的车辆,其性能可与高端INS装置相媲美。

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