Simultaneous State Estimation and Contact Detection for Legged Robots by Multiple-Model Kalman Filtering

This paper proposes an algorithm for combined contact detection and state estimation for legged robots. The algorithm models the robot's movement as a switched system, where different modes relate to different feet being in contact with the ground. The key element of our algorithm is an interacting multiple-model Kalman filter, which identifies the currently-active mode defining contacts, while estimating the state. The rationale for the proposed estimation framework is that contacts (and contact forces) impact the robot's state, and vice versa. We present validation studies with a quadruped using (i) the high-fidelity simulator Gazebo for a comparison with ground truth values and a baseline estimator, and (ii) hardware experiments with the Unitree Al robot. The simulation study shows that the proposed algorithm outperforms the baseline estimator, which does not simultaneous detect contacts. The hardware experiments showcase the applicability of the proposed algorithm and highlights the ability to detect contacts.