  Miniaturized autonomous unmanned aerial vehicles (UAVs) are an emerging and
trending topic. With their form factor as big as the palm of one hand, they can
reach spots otherwise inaccessible to bigger robots and safely operate in human
surroundings. The simple electronics aboard such robots (sub-100mW) make them
particularly cheap and attractive but pose significant challenges in enabling
onboard sophisticated intelligence. In this work, we leverage a novel neural
architecture search (NAS) technique to automatically identify several
Pareto-optimal convolutional neural networks (CNNs) for a visual pose
estimation task. Our work demonstrates how real-life and field-tested robotics
applications can concretely leverage NAS technologies to automatically and
efficiently optimize CNNs for the specific hardware constraints of small UAVs.
We deploy several NAS-optimized CNNs and run them in closed-loop aboard a 27-g
Crazyflie nano-UAV equipped with a parallel ultra-low power System-on-Chip. Our
results improve the State-of-the-Art by reducing the in-field control error of
32% while achieving a real-time onboard inference-rate of ~10Hz@10mW and
~50Hz@90mW.
