Invariant filtering for wheeled vehicle localization with unknown wheel radius and unknown GNSS lever arm

Paul ChauchatAMU SCI, AMU, LIS, DIAPRO, Silvère BonnabelCAOR, Axel Barrau
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Abstract

We consider the problem of observer design for a nonholonomic car (more generally a wheeled robot) equipped with wheel speeds with unknown wheel radius, and whose position is measured via a GNSS antenna placed at an unknown position in the car. In a tutorial and unified exposition, we recall the recent theory of two-frame systems within the field of invariant Kalman filtering. We then show how to adapt it geometrically to address the considered problem, although it seems at first sight out of its scope. This yields an invariant extended Kalman filter having autonomous error equations, and state-independent Jacobians, which is shown to work remarkably well in simulations. The proposed novel construction thus extends the application scope of invariant filtering.
未知车轮半径和未知全球导航卫星系统杠杆臂的轮式车辆定位不变滤波技术
我们考虑的是非全局性汽车(一般来说是轮式机器人)的观测器设计问题,该汽车配备了轮速未知的轮半径,其位置通过放置在汽车未知位置的 GNSS 天线测量。在教程和统一论述中,我们回顾了不变卡尔曼滤波领域中最新的双框架系统理论。我们展示了如何从几何角度对其进行调整,以解决所考虑的问题,尽管乍一看似乎超出了其范围。这就产生了一个具有自主误差方程和与状态无关的加可比系数的不变量下延卡尔曼滤波器,并在仿真中证明了其出色的工作性能。因此,所提出的新结构扩展了不变滤波的应用范围。
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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