An Experimental Comparison of Floating Base Estimators for Humanoid Robots with Flat Feet

TL;DR

This paper compares various flat-foot floating base estimators for humanoid robots, evaluating their consistency and accuracy through simulated and real-world experiments on the iCub platform.

Contribution

It provides an experimental comparison of state-of-the-art flat-foot filters, analyzing the impact of representation, error, and dynamics on filter performance.

Findings

Different filters show varying levels of consistency and accuracy.

Representation choice significantly affects filter performance.

Experimental results highlight strengths and weaknesses of each approach.

Abstract

Extended Kalman filtering is a common approach to achieve floating base estimation of a humanoid robot. These filters rely on measurements from an Inertial Measurement Unit (IMU) and relative forward kinematics for estimating the base position-and-orientation and its linear velocity along with the augmented states of feet position-and-orientation, thus giving them their name, flat-foot filters. However, the availability of only partial measurements often poses the question of consistency in the filter design. In this paper, we perform an experimental comparison of state-of-the-art flat-foot filters based on the representation choice of state, observation, matrix Lie group error and system dynamics evaluated for filter consistency and trajectory errors. The comparison is performed over simulated and real-world experiments conducted on the iCub humanoid platform.