Abstract
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.
Abstract (translated)
翻译： 自主纳米无人机（直径约10厘米），由于其基于极低功耗的TinyML大脑，能够应对真实世界环境。然而，由于其简单的传感器和计算单元，它们与较大 counterparts 相比还有很长的路要走。本文系统论文提出了一种新颖的深度学习-based 管道，将多感官输入（即低分辨率图像和8x8 深度图）与机器状态信息相结合，以解决人体姿态估计任务。感谢我们的设计，与在模拟环境中训练并在真实世界数据集上测试相比，所提出的系统 - 通过增加距离预测的 R^2 回归指标 - 提高了不知状态下的最先进基线的性能。
URL
https://arxiv.org/abs/2404.02567
