Accurate motion capture of aerial robots in 3-D 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 Ultrawideband (UWB), but these are costly and cumbersome to deploy, due to their requirement of multiple cameras/sensors 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, 3-D tracking is possible from a single point, greatly simplifying deployment. We develop a novel deep neural network and demonstrate decimeter level 3-D 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.
CubeDN: Real-time Drone Detection in 3D Space from Dual mmWave Radar Cubes
Department of Security and Crime Science, University College London（安全与犯罪科学系，伦敦大学学院）；Department of Computer Science, University College London（计算机科学系，伦敦大学学院）
KU Leuven（鲁汶大学）；Ghent University（根特大学）；imec（比利时微电子研究中心）
Frequency-modulated continuous-wave (FMCW) radar is a promising sensor technology for indoor drones as it provides range, angular as well as Doppler-velocity information about obstacles in the environment. Recently, deep learning approaches have been proposed for processing FMCW data, outperforming traditional detection techniques on range-Doppler or range-azimuth maps. However, these techniques come at a cost; for each novel task a deep neural network architecture has to be trained on high-dimensional input data, stressing both data bandwidth and processing budget. In this paper, we investiga…
As radar sensors are being miniaturized, there is a growing interest for using them in indoor sensing applications such as indoor drone obstacle avoidance. In those novel scenarios, radars must perform well in dense scenes with a large number of neighboring scatterers. Central to radar performance is the detection algorithm used to separate targets from the background noise and clutter. Traditionally, most radar systems use conventional CFAR detectors but their performance degrades in indoor scenarios with many reflectors. Inspired by the advances in non-linear target detection, we propose a n…
Ultra-High-Frequency Harmony: mmWave Radar and Event Camera Orchestrate Accurate Drone Landing
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Carnegie Mellon University（卡内基梅隆大学）；Meituan Academy of Robotics Shenzhen（美团深圳机器人研究院）；School of Software, Tsinghua University（清华大学软件学院）；Pengcheng Laboratory（鹏城实验室）；RISC-V International Open Source Laboratory（RISC-V国际开源实验室）
mmE-Loc: Facilitating Accurate Drone Landing with Ultra-High-Frequency Localization
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Carnegie Mellon University（卡内基梅隆大学）；Meituan Academy of Robotics Shenzhen and Meituan Inc.（美团机器人研究院（深圳）及美团公司）；School of Software and BNRist, Tsinghua University（清华大学软件学院）；Shenzhen Smart City Communication Co., Ltd.（深圳智慧城市通信有限公司）；Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；International Digital Economy Academy（国际数字经济 academy）
Drones are currently being explored for safety-critical applications where human agents are expected to evolve in their vicinity. In such applications, robust people avoidance must be provided by fusing a number of sensing modalities in order to avoid collisions. Currently however, people detection systems used on drones are solely based on standard cameras besides an emerging number of works discussing the fusion of imaging and event-based cameras. On the other hand, radar-based systems provide up-most robustness towards environmental conditions but do not provide complete information on thei…
Multi-Modal UAV Detection, Classification and Tracking Algorithm -- Technical Report for CVPR 2024 UG2 Challenge
Shanghai Jiao Tong University（上海交通大学）；JITRI（江苏集萃智能制造技术研究所有限公司）；Dalian University of Technology（大连理工大学）；University of Electronic Science and Technology of China（电子科技大学）；The University of Tokyo（东京大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
