A Low-Drift Legged Robot State-Estimation System Through Combined Physics-Informed Contact Estimation Network and Full Joint State

Abstract

Proprioceptive sensors are crucial for legged robots, as they provide reliable internal state information and are less affected by environmental disturbances. A robust proprioceptive base state estimator is essential for the localization and control capabilities of legged robots. Classical methods for estimation of legged robot state often use IMU integration for prediction and use the assumption of stationary foot contact for updates. However, they suffer from issues like IMU accelerometer noise from foot-end impacts, nonlinear foot-ground interactions, and sensor parameter uncertainties, which leads to estimation drift. To address these limitations, this paper proposes a novel system for estimating the low drift state of legged robots by combining contact and joint state estimation. Specifically, our method i) proposes a physics-informed contact estimation state network to obtain accurate contact states for legged robots, ii) estimates joint states of the legged robot and obtained body accelerations computed from joint accelerations, and iii) updates the base position, orientation, and velocity by gravitational acceleration components and the assumption of static contact points. Under standard operating conditions, experiments on both public and private datasets demonstrate that the proposed method outperforms state-of-the-art algorithms.