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 produc...

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Bibliographic Details
Published in:IEEE robotics and automation letters 2017-07, Vol.2 (3), p.1770-1777
Main Authors: Hausman, Karol, Preiss, James, Sukhatme, Gaurav S., Weiss, Stephan
Format: Article
Language:English
Subjects:
aerial robotics
Calibration and identification
Convergence
motion and path planning
Nonlinear systems
Observability
Robot sensing systems
State estimation
Trajectory optimization
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Summary: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.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2017.2647799