Virtual Radar: Real-Time Millimeter-Wave Radar Sensor Simulation for Perception-Driven Robotics

Schöffmann, Christian; Ubezio, Barnaba; Böhm, Christoph; Mühlbacher-Karrer, Stephan; Zangl, Hubert

doi:10.1109/lra.2021.3068916

Cited by 21 publications

(7 citation statements)

References 20 publications

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“…The physical environment in artificial scenarios includes common buildings, driving cars, and different weather conditions, while the social environment focuses on human behaviors and knowledge. Due to the involvement of social space, Descriptive Radars are able to generate massive more realistic synthetic data compared with the current digital twins' radars in CPS [16][17][18][19][20][21][22][23][24][25].…”

Section: Descriptive Radarsmentioning

confidence: 99%

“…With the rapid development of artificial intelligence and computer science, digital twins in cyber-physical systems (CPS) [13][14][15], which are regarded as the key to the next industrial revolution, are being used to construct digital radars in cyberspace to achieve intelligence. Radar models in CPS [16][17][18][19][20][21][22][23][24][25] have already been extensively researched and demonstrated to be effective in solving many problems, including generating virtual data for various downstream tasks [26][27][28][29][30][31][32][33][34] and closed-loop testing.…”

Section: Introductionmentioning

confidence: 99%

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Parallel Radars: From Digital Twins to Digital Intelligence for Smart Radar Systems

Liu

Shen

Fan

et al. 2022

Sensors

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Radar is widely employed in many applications, especially in autonomous driving. At present, radars are only designed as simple data collectors, and they are unable to meet new requirements for real-time and intelligent information processing as environmental complexity increases. It is inevitable that smart radar systems will need to be developed to deal with these challenges and digital twins in cyber-physical systems (CPS) have proven to be effective tools in many aspects. However, human involvement is closely related to radar technology and plays an important role in the operation and management of radars; thus, digital twins’ radars in CPS are insufficient to realize smart radar systems due to the inadequate consideration of human factors. ACP-based parallel intelligence in cyber-physical-social systems (CPSS) is used to construct a novel framework for smart radars, called Parallel Radars. A Parallel Radar consists of three main parts: a Descriptive Radar for constructing artificial radar systems in cyberspace, a Predictive Radar for conducting computational experiments with artificial systems, and a Prescriptive Radar for providing prescriptive control to both physical and artificial radars to complete parallel execution. To connect silos of data and protect data privacy, federated radars are proposed. Additionally, taking mines as an example, the application of Parallel Radars in autonomous driving is discussed in detail, and various experiments have been conducted to demonstrate the effectiveness of Parallel Radars.

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Section: Descriptive Radarsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parallel Radars: From Digital Twins to Digital Intelligence for Smart Radar Systems

Liu

Shen

Fan

et al. 2022

Sensors

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“…With the advent of high-performance computing, simulation became a key tool in robotics for algorithmic development, especially combined with learning frameworks. Recently, simulation pipelines have been proposed which generate photorealistic images, lidar and radar data [20]- [22]. Such simulation tools are promising candidates for sim-to-real learning methods in robotics.…”

Section: Related Workmentioning

confidence: 99%

CapSense: A Real-Time Capacitive Sensor Simulation Framework for Physical Human-Robot Interaction

Schoffmann

Erickson

Zangl

2022

IEEE Robot. Autom. Lett.

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This article presents CapSense, a real-time opensource capacitive sensor simulation framework for robotic applications. CapSense provides raw data of capacitive proximity sensors based on a fast and efficient 3D finite-element method (FEM) implementation. The proposed framework is interfaced to off-the-shelf robot and physics simulation environments to couple dynamic interaction of the environment with an electrostatic solver for capacitance computation in real-time. The FEM method proposed in this article relies on a static tetrahedral mesh of the sensor surrounding without a-posteriori re-meshing and achieves high update rates by an adaptive update step. CapSense is flexible due to various configuration parameters (i.e. number, size, shape and location of electrodes) and serves as a platform for investigation of capacitive sensors in robotic applications. By using the proposed framework, researchers can simulate capacitive sensors in different scenarios and investigate these sensors and their configuration prior to installation and fabrication of real hardware. The proposed framework opens new research opportunities via sim-to-real transfer of capacitive sensing. The simulation approach is validated by comparing realworld results of different scenarios with simulation results. In order to showcase the benefits of CapSense in physical Human-Robot Interaction (pHRI), the framework is evaluated in a robotic healthcare scenario.

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“…Existing radar simulators are often not designed for 3D imaging and have certain constraints. For example, the system in [9] generates range and Doppler information of the radar rather than the raw data, the system in [10] only supports single antenna data generation and cannot be used to estimate the AoA, and the system in [11] only supports up to four receivers in one direction and cannot be used for 3D imaging. In this research, a lightweight mmWave radar simulator is designed that supports raw data generation of a multi-antenna mmWave radar, configurable antenna parameters and layout, and customized scene construction using 3D human models with programmable motions.…”

Section: Introductionmentioning

confidence: 99%

Millimetre-wave Radar for Low-Cost 3D Imaging: A Performance Study

Cui¹,

Wu²,

Dahnoun³

2023

Preprint

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Millimetre-wave (mmWave) radars can generate 3D point clouds to represent objects in the scene. However, the accuracy and density of the generated point cloud can be lower than a laser sensor. Although researchers have used mmWave radars for various applications, there are few quantitative evaluations on the quality of the point cloud generated by the radar and there is a lack of a standard on how this quality can be assessed. This work aims to fill the gap in the literature. A radar simulator is built to evaluate the most common data processing chains of 3D point cloud construction and to examine the capability of the mmWave radar as a 3D imaging sensor under various factors. It will be shown that the radar detection can be noisy and have an imbalance distribution. To address the problem, a novel superresolution point cloud construction (SRPC) algorithm is proposed to improve the spatial resolution of the point cloud and is shown to be able to produce a more natural point cloud and reduce outliers.

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Virtual Radar: Real-Time Millimeter-Wave Radar Sensor Simulation for Perception-Driven Robotics

Cited by 21 publications

References 20 publications

Parallel Radars: From Digital Twins to Digital Intelligence for Smart Radar Systems

Parallel Radars: From Digital Twins to Digital Intelligence for Smart Radar Systems

CapSense: A Real-Time Capacitive Sensor Simulation Framework for Physical Human-Robot Interaction

Millimetre-wave Radar for Low-Cost 3D Imaging: A Performance Study

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