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arXiv 2409.07050cs.RO
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轮径未知且GNSS杠杆臂未知的轮式车辆定位不变滤波

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

Aix-Marseille Univ(艾克斯-马赛大学);CNRS(法国国家科学研究中心);OFFROAD(OFFROAD(研究机构));MINES Paris PSL(巴黎矿业学院PSL);PSL Research University(PSL研究大学)

Paul Chauchat, Silvère Bonnabel, Axel Barrau

AI总结:

针对轮径未知、GNSS天线安装位置未知的轮式机器人定位问题,本文调整两帧系统不变卡尔曼滤波理论,提出具有自治误差方程、状态无关雅可比矩阵的不变扩展卡尔曼滤波,仿真性能优异,拓展了不变滤波的应用范围。

摘要:

我们研究非完整约束汽车(更广义为轮式机器人)的观测器设计问题:该车辆配备轮速传感器但轮径未知,且通过安装在车内未知位置的GNSS天线测量位置。本文以教程式统一阐述,回顾不变卡尔曼滤波领域中最新的两帧系统理论。随后我们展示如何从几何层面调整该理论以解决上述问题,尽管该问题乍看并不在其适用范围内。由此得到的不变扩展卡尔曼滤波器具有自治误差方程和与状态无关的雅可比矩阵,仿真结果表明其性能优异。该新型构造拓展了不变滤波的应用范围。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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