论文标题
通过不变的卡尔曼过滤和干扰观察者对移动机器人的完全本体感受性感知状态估计
Fully Proprioceptive Slip-Velocity-Aware State Estimation for Mobile Robots via Invariant Kalman Filtering and Disturbance Observer
论文作者
论文摘要
本文使用不变的观察者设计理论和干扰观察者（DOB）开发了一种新颖的滑动估计器。提议的移动机器人状态估计器具有完全的本体感受，并将来自惯性测量单元和身体速度的数据结合在正确的不变延长的Kalman滤波器（RI-EKF）中。通过将滑移速度嵌入$ \ mathrm {se} _3（3）$矩阵Lie Group，开发的基于DOB的RI-EKF提供了实时速度和在不同地形上的滑移速度估计。使用沙哑的机器人实验结果证实了该方法在估计可观察状态变量方面的数学推导和有效性。开源软件可供下载和复制提出的结果。
This paper develops a novel slip estimator using the invariant observer design theory and Disturbance Observer (DOB). The proposed state estimator for mobile robots is fully proprioceptive and combines data from an inertial measurement unit and body velocity within a Right Invariant Extended Kalman Filter (RI-EKF). By embedding the slip velocity into $\mathrm{SE}_3(3)$ matrix Lie group, the developed DOB-based RI-EKF provides real-time velocity and slip velocity estimates on different terrains. Experimental results using a Husky wheeled robot confirm the mathematical derivations and effectiveness of the proposed method in estimating the observable state variables. Open-source software is available for download and reproducing the presented results.
