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

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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.

Original languageEnglish
Article number9478211
Pages (from-to)6725-6732
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume6
Issue number4
DOIs
Publication statusPublished - 2021

Keywords

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

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