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Journal Articles IEEE Transactions on Robotics Year : 2019

Online Optimal Perception-Aware Trajectory Generation


This paper proposes an online optimal active perception strategy for differentially flat systems meant to maximize the information collected via the available measurements along the planned trajectory. The goal is to generate online a trajectory that minimizes the maximum state estimation uncertainty provided by the employed observer. To quantify the richness of the acquired information about the current state, the smallest eigenvalue of the Constructibility Gramian is adopted as a metric. We use B-Splines for parametrizing the trajectory of the flat outputs andwe exploit a constrained gradient descent strategy for optimizing online the location of the B-Spline control points in order to actively maximize the information gathered over the whole planning horizon. To show the effectiveness of our method in maximizing the estimation accuracy, we consider two case studies involving a unicycle and a quadrotor that need to estimate their poses while measuring two distances w.r.t. two fixed landmarks. Concurrent estimation of calibration/environment parameters is also considered for illustrating how the proposed method copes with instances of active self-calibration and map building.
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hal-02278900 , version 1 (04-09-2019)



Paolo Salaris, Marco Cognetti, Riccardo Spica, Paolo Robuffo Giordano. Online Optimal Perception-Aware Trajectory Generation. IEEE Transactions on Robotics, 2019, pp.1-16. ⟨10.1109/TRO.2019.2931137⟩. ⟨hal-02278900⟩
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