Inertial Sensor-Based Humanoid Joint State Estimation

TL;DR

This paper introduces a method to estimate humanoid robot joint states using link-mounted IMUs, eliminating the need for global pose information and improving control performance through sensor fusion and calibration routines.

Contribution

It presents a novel IMU-based joint state estimation method with calibration and bias compensation, enhancing accuracy without global pose data.

Findings

Joint velocity estimates are less noisy and delayed.

The method outperforms numerical differentiation in control tasks.

Experiments on a hydraulic humanoid validate the approach.

Abstract

This work presents methods for the determination of a humanoid robot's joint velocities and accelerations directly from link-mounted Inertial Measurement Units (IMUs) each containing a three-axis gyroscope and a three-axis accelerometer. No information about the global pose of the floating base or its links is required and precise knowledge of the link IMU poses is not necessary due to presented calibration routines. Additionally, a filter is introduced to fuse gyroscope angular velocities with joint position measurements and compensate the computed joint velocities for time-varying gyroscope biases. The resulting joint velocities are subject to less noise and delay than filtered velocities computed from numerical differentiation of joint potentiometer signals, leading to superior performance in joint feedback control as demonstrated in experiments performed on a SARCOS hydraulic humanoid.