Probabilistic Contact Estimation and Impact Detection for State Estimation of Quadruped Robots

Abstract

Reliable state estimation is crucial for stable planning and control of legged locomotion. A fundamental component of a state estimator in legged platforms is Leg Odometry, which only requires information about kinematics and contacts. Many legged robots use dedicated sensors on each foot to detect ground contacts. However, this choice is impractical for many agile legged robots in field operations, as these sensors often degrade and break. Instead, this paper focuses on the development of a robust Leg Odometry module, which does not require contact sensors. The module estimates the probability of reliable contact and detects foot impacts using internal force sensing. This knowledge is then used to improve the kinematics-inertial state estimate of the robot's base. We show how our approach can reach comparable performance to systems with foot sensors. Extensive experimental results lasting over 1 h are presented on our 85 kg quadrupedal robot HyQ carrying out a variety of gaits.