  Artificial intelligence-powered pocket-sized air robots have the potential to
revolutionize the Internet-of-Things ecosystem, acting as autonomous,
unobtrusive, and ubiquitous smart sensors. With a few cm$^{2}$ form-factor,
nano-sized unmanned aerial vehicles (UAVs) are the natural befit for indoor
human-drone interaction missions, as the pose estimation task we address in
this work. However, this scenario is challenged by the nano-UAVs' limited
payload and computational power that severely relegates the onboard brain to
the sub-100 mW microcontroller unit-class. Our work stands at the intersection
of the novel parallel ultra-low-power (PULP) architectural paradigm and our
general development methodology for deep neural network (DNN) visual pipelines,
i.e., covering from perception to control. Addressing the DNN model design,
from training and dataset augmentation to 8-bit quantization and deployment, we
demonstrate how a PULP-based processor, aboard a nano-UAV, is sufficient for
the real-time execution (up to 135 frame/s) of our novel DNN, called
PULP-Frontnet. We showcase how, scaling our model's memory and computational
requirement, we can significantly improve the onboard inference (top energy
efficiency of 0.43 mJ/frame) with no compromise in the quality-of-result vs. a
resource-unconstrained baseline (i.e., full-precision DNN). Field experiments
demonstrate a closed-loop top-notch autonomous navigation capability, with a
heavily resource-constrained 27-gram Crazyflie 2.1 nano-quadrotor. Compared
against the control performance achieved using an ideal sensing setup, onboard
relative pose inference yields excellent drone behavior in terms of median
absolute errors, such as positional (onboard: 41 cm, ideal: 26 cm) and angular
(onboard: 3.7$^{\circ}$, ideal: 4.1$^{\circ}$).
