Proprioceptive Sensor Fusion for Quadruped Robot State Estimation

Abstract

Estimation of a quadruped's state is fundamentally important to its operation. In this paper we develop a low-level state estimator for quadrupedal robots that includes attitude, odometry, ground reaction forces, and contact detection. The state estimator is divided into three parts. First, a nonlinear observer estimates attitude by fusing inertial measurements. The attitude estimator is globally exponentially stable and is able to initialize with large errors in the initial state estimates whereas a state-of-the-art EKF would diverge. This is practical for situations when the robot has fallen over and needs to start from its side. Second, leg odometry is calculated with encoders, force sensors, and torque sensors in the robot's joints. Lastly, the leg odometry and inertial measurements are fused to obtain linear position and velocity. We experimentally validate the state estimator using a novel dataset from the HyQ robot. For the entirety of the experiment the estimated attitude matched the ground truth data and had a root mean square error (RMSE) of [2 1 5] deg, the velocity estimates has a RMSE of [0.11 0.15 0.04] m/s, and the position estimates, which are unobservable, drifted on average [2 1 8] mm/s.