State estimation for biped robots using multibody dynamics

This paper introduces a new state estimator for biped robots fusing encoder, inertial and force torque measurements. The estimator is implemented as a Kalman filter that uses the dynamical model of the linear inverted pendulum with the center of mass (CoM) state as output. In order to compensate for disturbances and model errors we extend the model by a state for the external force and an additional input which is calculated from the dynamics error in pattern generation. Several simulation results underline the effectiveness of the proposed filter and show its robustness against disturbances. Experimental results and an application example validate the method under real world conditions.