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.
Original language | English |
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Article number | 9478211 |
Pages (from-to) | 6725-6732 |
Number of pages | 8 |
Journal | IEEE Robotics and Automation Letters |
Volume | 6 |
Issue number | 4 |
DOIs | |
Publication status | Published - 2021 |
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-careOtherwise 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.
Keywords
- aerial systems: perception and autonomy
- localization
- optimization and optimal control
- Sensor fusion
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Data underlying the publication: "A Computationally Efficient Moving Horizon Estimator for UWB Localization on Small Quadrotors""
Pfeiffer, S. U. (Creator), de Wagter, C. (Creator) & de Croon, G. C. H. E. (Creator), TU Delft - 4TU.ResearchData, 30 Jun 2021
DOI: 10.4121/14827680
Dataset/Software: Dataset