Observability-Aware Trajectory Optimization for Self-Calibration With Application to UAVs

We study the nonlinear observability of a system's states in view of how well they are observable and what control inputs would improve the convergence of their estimates. We use these insights to develop an observability-aware trajectory-optimization framework for nonlinear systems that produces trajectories well suited for self-calibration. Our method reasons about the quality of observability while respecting system dynamics and motion constraints to yield the optimal trajectory for rapid convergence of the self-calibration states (or other user-chosen states). Self-calibration trials with a real and a simulated quadrotor provide compelling evidence that the proposed method is both faster and more accurate compared to other state-of-the-art approaches.