Invariant Filtering for Legged Humanoid Locomotion on Dynamic Rigid Surfaces

TL;DR

This paper presents an invariant extended Kalman filter designed for accurate state estimation of legged robots on dynamic rigid surfaces, addressing nonstationary contact points and hybrid behaviors, with proven convergence and validated experiments.

Contribution

It introduces a novel invariant EKF that explicitly handles nonstationary contact points and hybrid behaviors for DRS locomotion, with theoretical convergence guarantees.

Findings

Effective state estimation on dynamic surfaces demonstrated

Filter remains robust under large estimation errors

Experimental validation on humanoid robot walking on pitching treadmill

Abstract

State estimation for legged locomotion over a dynamic rigid surface (DRS), which is a rigid surface moving in the world frame (e.g., ships, aircraft, and trains), remains an under-explored problem. This paper introduces an invariant extended Kalman filter that estimates the robot's pose and velocity during DRS locomotion by using common sensors of legged robots (e.g., inertial measurement units (IMU), joint encoders, and RDB-D camera). A key feature of the filter lies in that it explicitly addresses the nonstationary surface-foot contact point and the hybrid robot behaviors. Another key feature is that, in the absence of IMU biases, the filter satisfies the attractive group affine and invariant observation conditions, and is thus provably convergent for the deterministic continuous phases. The observability analysis is performed to reveal the effects of DRS movement on the state observability, and the convergence property of the hybrid, deterministic filter system is examined for the observable state variables. Experiments of a Digit humanoid robot walking on a pitching treadmill validate the effectiveness of the proposed filter under large estimation errors and moderate DRS movement. The video of the experiments can be found at: https://youtu.be/ScQIBFUSKzo.