State estimation of a walking humanoid robot

Abstract

This paper compares two approaches to designing Kalman Filters for walking systems. The first design uses Linear Inverted Pendulum Model (LIPM) dynamics, and the other design uses a more complete Planar dynamics. The filter based on the simpler LIPM design is more robust to modeling error. The more complex design estimates center of mass height and joint velocities, and tracks horizontal center of mass translation more accurately. We also investigate different ways of handling contact states and using force sensing in state estimation. In the LIPM filter, force sensing is used to determine contact states and tune filter parameters. In the Planar filter, force sensing is used to select the proper measurement equation.