Proprioceptive Invariant State Estimation for Humanoid Robots on Non-Inertial Ground
It enables humanoid robots to operate on non-inertial surfaces (e.g., ships, moving platforms) without external sensors, addressing a key limitation for real-world deployment.
This paper introduces an invariant extended Kalman filter for humanoid robot state estimation on moving ground using only proprioceptive sensors, achieving a 96% faster convergence rate and 80% lower position error compared to existing methods, with walking errors under 9 cm.
This paper presents an invariant extended Kalman filtering (InEKF) approach for real-time state estimation of humanoid robots operating on non-inertial ground using only onboard proprioceptive sensing. The proposed approach estimates the robot's base position and velocity relative to the moving ground frame without requiring direct measurements of ground motion or externally mounted sensors. By exploiting kinematic constraints at the stance foot through foot-mounted IMUs, the filter accounts for ground-induced nonlinearities in the process and measurement models while remaining fully proprioceptive. The estimator is formulated to admit a right-invariant measurement model, enabling favorable error dynamics under large initial uncertainties. Observability analysis establishes conditions under which the robot's relative base position and velocity are observable with respect to the non-inertial ground frame. Experiments with the Digit humanoid robot standing and squatting atop a swaying and pitching ground showcase a 96% speedup in convergence rate and an 80% reduction in position estimate errors over existing InEKFs. Walking experiments on a uni-axially rotating ground achieve an average estimation error of less than 9 cm for an initial error of up to 1 m.