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

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