  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 (SNN) capable of
estimating the pitch and roll angles of a quadrotor in highly dynamic movements
from 6-degree of freedom Inertial Measurement Unit (IMU) 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 towards
energy-efficient, fully autonomous control of quadrotors with dedicated
neuromorphic computing systems.
