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.

翻译：本文利用不变观测器设计理论与扰动观测器，提出了一种新型滑移估计器。所提出的移动机器人状态估计器完全依赖本体感知，并在右不变扩展卡尔曼滤波框架内融合惯性测量单元与本体速度数据。通过将滑移速度嵌入 $\mathrm{SE}_3(3)$ 矩阵李群，所开发的基于扰动观测器的右不变扩展卡尔曼滤波能够实时估计不同地形上的速度与滑移速度。使用Husky轮式机器人进行的实验结果验证了数学推导及所提方法在估计可观状态变量方面的有效性。开源软件可供下载并复现文中结果。