Experimental Evaluation of Simple Estimators for Humanoid Robots

This paper introduces and evaluates a family of new simple estimators to reconstruct the pose and velocity of the floating base. The estimation of the floating-base state is a critical challenge to whole-body control methods that rely on full-state information in high-rate feedback. Although the kinematics of grounded limbs may be used to estimate the pose and velocity of the body, modelling errors from ground irregularity, foot slip, and structural flexibilities limit the utility of estimation from kinematics alone. These difficulties have motivated the development of sensor fusion methods to augment body-mounted IMUs with kinematic measurements. Existing methods often rely on extended Kalman filtering, which lack convergence guarantees and may present difficulties in tuning. This paper proposes two new simplifications to the floating-base state estimation problem that make use of robust off-the-shelf orientation estimators to bootstrap development. Experiments for in-place balance and walking with the HRP-2 show that the simplifications yield results on par with the accuracy reported in the literature for other methods. As further benefits, the structure of the proposed estimators prevents divergence of the estimates, simplifies tuning, and admits efficient computation. These benefits are envisioned to help accelerate the development of baseline estimators in future humanoids.