Fully Proprioceptive Slip-Velocity-Aware State Estimation for Mobile Robots via Invariant Kalman Filtering and Disturbance Observer

TL;DR

This paper introduces a fully proprioceptive, slip-velocity-aware state estimator for mobile robots using invariant Kalman filtering and disturbance observer techniques, enabling real-time slip and velocity estimation across terrains.

Contribution

It develops a novel slip estimator based on invariant observer design and disturbance observer, integrated into a RI-EKF on a Lie group for improved state estimation.

Findings

Accurately estimates slip and velocity in real-time.

Validated on Husky robot with successful experimental results.

Provides open-source software for reproducibility.

Abstract

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.