Neuromorphic computing for attitude estimation onboard quadrotors

S. Stroobants, J.J.G. Dupeyroux, G.C.H.E. de Croon

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
115 Downloads (Pure)

Abstract

Compelling evidence has been given for the high energy efficiency and update rates of neuromorphic processors, with performance beyond what standard Von Neumann architectures can achieve. Such promising features could be advantageous in critical embedded systems, especially in robotics. To date, the constraints inherent in robots (e.g., size and weight, battery autonomy, available sensors, computing resources, processing time, etc), and particularly in aerial vehicles, severely hamper the performance of fully-autonomous on-board control, including sensor processing and state estimation. In this work, we propose a spiking neural network capable of estimating the pitch and roll angles of a quadrotor in highly dynamic movements from six-degree of freedom inertial measurement unit data. With only 150 neurons and a limited training dataset obtained using a quadrotor in a real world setup, the network shows competitive results as compared to state-of-the-art, non-neuromorphic attitude estimators. The proposed architecture was successfully tested on the Loihi neuromorphic processor on-board a quadrotor to estimate the attitude when flying. Our results show the robustness of neuromorphic attitude estimation and pave the way toward energy-efficient, fully autonomous control of quadrotors with dedicated neuromorphic computing systems.

Original languageEnglish
Article number034005
Number of pages14
Journal Neuromorphic Computing and Engineering
Volume2
Issue number3
DOIs
Publication statusPublished - 2022

Keywords

  • neuromorphic computing
  • spiking neural networks (SNN)
  • unmanned aerial vehicles (UAVs)
  • micro air vehicles (MAVs)
  • neuromorphic processor

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