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

Sven Pfeiffer; Christophe De Wagter; Guido C.H.E.De Croon

doi:10.1109/LRA.2021.3095519

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

Sven Pfeiffer^*, Christophe De Wagter, Guido C.H.E.De Croon

^*Corresponding author for this work

Control & Simulation

Research output: Contribution to journal › Article › Scientific › peer-review

8 Citations (Scopus)

303 Downloads (Pure)

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
Article number	9478211
Pages (from-to)	6725-6732
Number of pages	8
Journal	IEEE Robotics and Automation Letters
Volume	6
Issue number	4
DOIs	https://doi.org/10.1109/LRA.2021.3095519
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-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.

Keywords

aerial systems: perception and autonomy
localization
optimization and optimal control
Sensor fusion

Access to Document

10.1109/LRA.2021.3095519

A_Computationally_Efficient_Moving_Horizon_Estimator_for_Ultra_Wideband_Localization_on_Small_QuadrotorsFinal published version, 1.21 MB

Cite this

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title = "A Computationally Efficient Moving Horizon Estimator for Ultra-Wideband Localization on Small Quadrotors",

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.",

keywords = "aerial systems: perception and autonomy, localization, optimization and optimal control, Sensor fusion",

author = "Sven Pfeiffer and Wagter, {Christophe De} and Croon, {Guido C.H.E.De}",

note = "Green Open Access added to TU Delft Institutional Repository {\textquoteleft}You share, we take care!{\textquoteright} – 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. ",

year = "2021",

doi = "10.1109/LRA.2021.3095519",

language = "English",

volume = "6",

pages = "6725--6732",

journal = "IEEE Robotics and Automation Letters",

issn = "2377-3766",

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T1 - A Computationally Efficient Moving Horizon Estimator for Ultra-Wideband Localization on Small Quadrotors

AU - Pfeiffer, Sven

AU - Wagter, Christophe De

AU - Croon, Guido C.H.E.De

N1 - 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.

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - aerial systems: perception and autonomy

KW - localization

KW - optimization and optimal control

KW - Sensor fusion

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M3 - Article

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SN - 2377-3766

VL - 6

SP - 6725

EP - 6732

JO - IEEE Robotics and Automation Letters

JF - IEEE Robotics and Automation Letters

IS - 4

M1 - 9478211

ER -

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

Abstract

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Range-Based Localization for Swarms of Micro Air Vehicles

Data underlying the publication: "A Computationally Efficient Moving Horizon Estimator for UWB Localization on Small Quadrotors""

Cite this

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

Abstract

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Research output

Range-Based Localization for Swarms of Micro Air Vehicles

Datasets

Data underlying the publication: "A Computationally Efficient Moving Horizon Estimator for UWB Localization on Small Quadrotors""

Cite this