Attitude Estimation for Dynamic Legged Locomotion Using Range and Inertial Sensors

Abstract

Legged robots offer exceptional mobility in uncharted terrains. Their dynamic nature yields unrivaled mobility, but serves to destabilize the motion estimation process that underlies legged operations. In particular, the discontinuous foot fall patterns and flight phases result in severe impulses, which, in turn, result in excessive accumulation of drift by inertial sensors. Ground range measurements, amongst several others, are robust to this drift yet are limited in application due to their low-bandwidth and sensitivity to ground conditions. In considering the attitude estimation problem for this dynamic legged locomotion, this paper develops a pose calculation method based on ground range measurements. This is used in conjunction with a hybrid Extended Kalman Filter that takes advantage of the ballistic nature of the flight phases. Results indicate that this combination provides rapid, robust estimates of attitude necessary for extended dynamic legged operations. In single leg experiments, which were conducted using low-cost sensing hardware, this method had an RMS error of < 1 °, half that of a non-hybrid EKF approach.