Fusing Multi-sensor Input with State Information on TinyML Brains for Autonomous Nano-drones
<|reference_start|>Fusing Multi-sensor Input with State Information on TinyML Brains for Autonomous Nano-drones: Autonomous nano-drones (~10 cm in diameter), thanks to their ultra-low power TinyML-based brains, are capable of coping with real-world environments. However, due to their simplified sensors and compute units, they are still far from the sense-and-act capabilities shown in their bigger counterparts. This system paper presents a novel deep learning-based pipeline that fuses multi-sensorial input (i.e., low-resolution images and 8x8 depth map) with the robot's state information to tackle a human pose estimation task. Thanks to our design, the proposed system -- trained in simulation and tested on a real-world dataset -- improves a state-unaware State-of-the-Art baseline by increasing the R^2 regression metric up to 0.10 on the distance's prediction.<|reference_end|>
@article{crupi2024fusing,
    title={Fusing Multi-sensor Input with State Information on TinyML Brains for
  Autonomous Nano-drones},
    author={Luca Crupi, Elia Cereda, Daniele Palossi},
    journal={arXiv preprint arXiv:2404.02567},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.02567},
    primaryClass={cs.RO}
}