无人机
计算机科学
软件部署
极高频率
实时计算
雷达
钥匙(锁)
利用
运动捕捉
机器人
计算机硬件
人工智能
运动(物理)
电信
遗传学
计算机安全
生物
操作系统
作者
Peijun Zhao,Chris Xiaoxuan Lu,Bing Wang,Niki Trigoni,Andrew Markham
标识
DOI:10.1109/icra48506.2021.9561738
摘要
Accurate motion capture of aerial robots in 3D is a key enabler for autonomous operation in indoor environments such as warehouses or factories, as well as driving forward research in these areas. The most commonly used solutions at present are optical motion capture (e.g. VICON) and Ultrawide-band (UWB), but these are costly and cumbersome to deploy, due to their requirement of multiple cameras/anchors spaced around the tracking area. They also require the drone to be modified to carry an active or passive marker. In this work, we present an inexpensive system that can be rapidly installed, based on single-chip millimeter wave (mmWave) radar. Importantly, the drone does not need to be modified or equipped with any markers, as we exploit the Doppler signals from the rotating propellers. Furthermore, 3D tracking is possible from a single point, greatly simplifying deployment. We develop a novel deep neural network and demonstrate decimeter level 3D tracking at 10Hz, achieving better performance than classical baselines. Our hope is that this low-cost system will act to catalyse inexpensive drone research and increased autonomy.
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