Multi-IMU Proprioceptive State Estimator for Humanoid Robots

TL;DR

This paper introduces a multi-IMU extended Kalman filter-based state estimator for humanoid robots that effectively handles various contact configurations, improves kinematic modeling, and reduces drift during walking.

Contribution

It presents a novel multi-IMU state estimation method that manages arbitrary contact states and estimates structural deformations, enhancing accuracy over single-IMU approaches.

Findings

Low drift in trajectory estimates

Better performance than single-IMU filters

Accurate elevation maps with minimal distortion

Abstract

Algorithms for state estimation of humanoid robots usually assume that the feet remain flat and in a constant position while in contact with the ground. However, this hypothesis is easily violated while walking, especially for human-like gaits with heel-toe motion. This reduces the time during which the contact assumption can be used, or requires higher variances to account for errors. In this paper, we present a novel state estimator based on the extended Kalman filter that can properly handle any contact configuration. We consider multiple inertial measurement units (IMUs) distributed throughout the robot's structure, including on both feet, which are used to track multiple bodies of the robot. This multi-IMU instrumentation setup also has the advantage of allowing the deformations in the robot's structure to be estimated, improving the kinematic model used in the filter. The proposed approach is validated experimentally on the exoskeleton Atalante and is shown to present low drift, performing better than similar single-IMU filters. The obtained trajectory estimates are accurate enough to construct elevation maps that have little distortion with respect to the ground truth.