Proprioceptive State Estimation for Quadruped Robots using Invariant Kalman Filtering and Scale-Variant Robust Cost Functions

TL;DR

This paper introduces a novel invariant extended Kalman filter for quadruped robots that leverages proprioceptive sensors and robust cost functions, significantly reducing pose drift in challenging terrains.

Contribution

It presents a new invariant extended Kalman filter methodology that improves state estimation accuracy for legged robots using only proprioceptive sensors and robust measurement updates.

Findings

Pose drift reduced by up to 40% over 450m trajectories.

Method outperforms existing invariant EKF in experiments.

Effective in uneven and slippery terrain conditions.

Abstract

Accurate state estimation is crucial for legged robot locomotion, as it provides the necessary information to allow control and navigation. However, it is also challenging, especially in scenarios with uneven and slippery terrain. This paper presents a new Invariant Extended Kalman filter for legged robot state estimation using only proprioceptive sensors. We formulate the methodology by combining recent advances in state estimation theory with the use of robust cost functions in the measurement update. We tested our methodology on quadruped robots through experiments and public datasets, showing that we can obtain a pose drift up to 40% lower in trajectories covering a distance of over 450m, in comparison with a state-of-the-art Invariant Extended Kalman filter.