Dynamic state estimation using Quadratic Programming

We propose a framework for using full-body dynamics for humanoid state estimation. It is formulated as an optimization problem and solved with Quadratic Programming (QP). This formulation provides two main advantages over a nonlinear Kalman filter for dynamic state estimation. QP does not require the dynamic system to be written in the state space form, and it handles equality and inequality constraints naturally. The QP state estimator considers modeling error as part of the optimization vector and includes it in the cost function. The proposed QP state estimator is tested on a Boston Dynamics Atlas humanoid robot.