  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.
