Robust vehicle localization with GPS dropouts

Salman Ali Shaukat, K. Munawar, M. Arif, A. I. Bhatti, U. I. Bhatti, U. Al-Saggaf
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引用次数: 6

Abstract

Vehicle navigation systems rely on the correct positioning information and GPS is the best solution. However, in situations of GPS outage, like in tunnels and dense woods, the vehicle can lose track quickly. Modern vehicle navigation systems are mostly based on the vehicle dynamics; proprietary algorithms keep navigating for quite some time. But these solutions are expensive and add much to a vehicle's cost. This paper proposes a Kalman filter based dead-reckoning algorithm that fuses GPS information with the orientation information from a cheap IMU/INS, and the vehicle's speed accessed from its ECU. This low-cost system uses GPS and IMU/INS in a loosely coupled manner along with vehicle's speed and keeps supplying a quite accurate position information with GPS outage for significantly long intervals. With proper tuning and initialization, the proposed scheme has a potential to keep working in a completely GPS-denied situation.
具有GPS丢失的鲁棒车辆定位
车辆导航系统依赖于正确的定位信息,GPS是最好的解决方案。然而,在GPS中断的情况下,比如在隧道和茂密的树林中,车辆可能会很快失去跟踪。现代车辆导航系统大多是基于车辆动力学的;专有算法在相当长的一段时间内一直在导航。但这些解决方案都很昂贵,而且会增加汽车的成本。本文提出了一种基于卡尔曼滤波的航位推算算法,该算法将GPS信息与廉价IMU/INS的方向信息以及ECU获取的车辆速度信息融合在一起。这种低成本的系统使用GPS和IMU/INS以松散耦合的方式与车辆的速度结合,在GPS中断相当长的时间间隔内保持提供相当准确的位置信息。通过适当的调优和初始化,所提出的方案有可能在完全拒绝gps的情况下继续工作。
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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