Comparison of EKF-Based Floating Base Estimators for Humanoid Robots with Flat Feet

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. We refer to such filters as 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. It is observed that filters on Lie groups that exploit properties of invariant filtering tend to perform better as consistent estimators while discrete-time filters in general provide higher accuracy along observable directions.