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On the role of process models in autonomous land vehicle navigation systems

机译:过程模型在自主陆地车辆导航系统中的作用

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This paper examines the role played by vehicle models and their impact on the performance of sensor-based navigation systems for autonomous land vehicles. In a navigation system, information from internal and external vehicle sensors is combined to estimate the motion of the vehicle. However, while the issue of sensing and effects of sensor accuracy have been widely studied, there are few results or insights into the complementary role played by the vehicle model. This paper has two main contributions: a theoretical analysis of the role of the vehicle model in navigation system performance, and an empirical study of three models of increasing complexity, used in a navigation system for a conventional road vehicle. The theoretical analysis focuses on understanding the effect of estimation errors caused by approximations to the "true" vehicle model. It shows that while substantial performance improvements can be obtained from better vehicle modeling, there is, in general, no definitive "best" model for such complex nonlinear estimation problems. The empirical study shows that an appropriate choice of a higher order model can lead to significant improvements in the performance of the navigation system. However, the highest order model suffers from problems related to the observability of some of its parameters. We show how this problem can be overcome through the imposition of weak constraints.
机译:本文研究了车辆模型所扮演的角色,以及它们对自主式陆地车辆基于传感器的导航系统性能的影响。在导航系统中,来自内部和外部车辆传感器的信息被组合在一起以估计车辆的运动。但是,尽管对传感和传感器精度影响的问题进行了广泛的研究,但是对于车辆模型所起的互补作用却鲜有结果或见解。本文有两个主要贡献:对车辆模型在导航系统性能中的作用进行理论分析,以及对在常规公路车辆的导航系统中使用的三种复杂程度不断提高的模型进行的实证研究。理论分析着重于理解由近似“真实”车辆模型引起的估计误差的影响。它表明,虽然可以通过更好的车辆建模获得实质性的性能改进,但通常没有针对此类复杂非线性估计问题的确定的“最佳”模型。实证研究表明,适当选择高阶模型可以显着改善导航系统的性能。但是,最高阶模型存在与某些参数的可观察性有关的问题。我们展示了如何通过施加弱约束来克服此问题。

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