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A Robust Hybrid Multisource Data Fusion Approach for Vehicle Localization

机译:用于车辆定位的鲁棒混合多源数据融合方法

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摘要

In this paper, an innovative collaborative data fusion approach to ego-vehicle localization is presented. This approach called Optimized Kalman Swarm (OKS) is a data fusion and filtering method, fusing data from a low cost GPS, an INS, an Odometer and a Steering wheel angle encoder. The OKS is developed addressing the challenge of managing reactivity and robustness during a real time ego-localization process. For ego-vehicle localization, especially for highly dynamic on-road maneuvers, a filter needs to be robust and reactive at the same time. In these situations, the balance between reactivity and robustness concepts is crucial. The OKS filter represents an intelligent cooperative-reactive localization algorithm inspired by dynamic Particle Swarm Optimization (PSO). It combines advantages coming from two filters: Particle Filter (PF) and Extended Kalman filter (EKF). The OKS is tested using real embedded sensors data collected in the Satory’s test tracks. The OKS is also compared with both the well-known EKF and the Particle Filters (PF). The results show the efficiency of the OKS for a high dynamic driving scenario with damaged and low quality GPS data.
机译:在本文中,提出了一种创新的协作数据融合方法来进行车载定位。这种称为优化卡尔曼群(OKS)的方法是一种数据融合和过滤方法,可融合来自低成本GPS,INS,里程表和方向盘角度编码器的数据。开发OKS是为了应对在实时自我定位过程中管理反应性和鲁棒性的挑战。对于自我车辆定位,特别是对于高度动态的公路机动,滤波器需要同时具有鲁棒性和反应性。在这些情况下,反应性和耐用性概念之间的平衡至关重要。 OKS滤波器表示一种受动态粒子群优化(PSO)启发的智能协作反应式本地化算法。它结合了两个滤波器的优点:粒子滤波器(PF)和扩展卡尔曼滤波器(EKF)。使用Satory测试轨迹中收集的真实嵌入式传感器数据对OKS进行测试。还可以将OKS与著名的EKF和粒子过滤器(PF)进行比较。结果表明,OKS在高动态驾驶情况下具有损坏的低质量GPS数据的效率。

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