A Computationally Efficient Moving Horizon Estimator for Ultra-Wideband Localization on Small Quadrotors

Journal Article (2021)
Author(s)

S.U. Pfeiffer (TU Delft - Control & Simulation)

C de Wagter (TU Delft - Control & Simulation)

G. C. H. E. de Croon (TU Delft - Control & Simulation)

Research Group
Control & Simulation
Copyright
© 2021 S.U. Pfeiffer, C. de Wagter, G.C.H.E. de Croon
DOI related publication
https://doi.org/10.1109/LRA.2021.3095519
More Info
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Publication Year
2021
Language
English
Copyright
© 2021 S.U. Pfeiffer, C. de Wagter, G.C.H.E. de Croon
Related content
Research Group
Control & Simulation
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. @en
Issue number
4
Volume number
6
Pages (from-to)
6725-6732
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Abstract

We present a computationally efficient moving horizon estimator that allows for real-time localization using Ultra-Wideband measurements on small quadrotors. The estimator uses only a single iteration of a simple gradient descent method to optimize the state estimate based on past measurements, while using random sample consensus to reject outliers. We compare our algorithm to a state-of-the-art Extended Kalman Filter and show its advantages when dealing with heavy-tailed noise, which is frequently encountered in Ultra-Wideband ranging. Furthermore, we analyze the algorithm's performance when reducing the number of beacons for measurements and we implement the code on a 30 g Crazyflie drone, to show its ability to run on computationally limited devices.

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