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Nano-quadcopters are versatile platforms attracting the interest of bothacademia and industry. Their tiny form factor, i.e., $\,$10 cm diameter, makesthem particularly useful in narrow scenarios and harmless in human proximity.However, these advantages come at the price of ultra-constrained onboardcomputational and sensorial resources for autonomous operations. This workaddresses the task of estimating human pose aboard nano-drones by fusing depthand images in a novel CNN exclusively trained in simulation yet capable ofrobust predictions in the real world. We extend a commercial off-the-shelf(COTS) Crazyflie nano-drone -- equipped with a 320$\times$240 px camera and anultra-low-power System-on-Chip -- with a novel multi-zone (8$\times$8) depthsensor. We design and compare different deep-learning models that fuse depthand image inputs. Our models are trained exclusively on simulated data for bothinputs, and transfer well to the real world: field testing shows an improvementof 58% and 51% of our depth+camera system w.r.t. a camera-only State-of-the-Artbaseline on the horizontal and angular mean pose errors, respectively. Ourprototype is based on COTS components, which facilitates reproducibility andadoption of this novel class of systems.

Sim-to-Real Vision-depth Fusion CNNs for Robust Pose Estimation Aboard Autonomous Nano-quadcopter