ColoRadar: The direct 3D millimeter wave radar dataset

Kramer, Andrew; Kyle, Harlow,; Williams, C. R.; Heckman, Christoffer

doi:10.1177/02783649211068535

Cited by 39 publications

(9 citation statements)

References 24 publications

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“…Moreover, this approach necessitates extensive data for training and validation. Building test scenarios to establish datasets or using publicly available radar datasets, such as ColoRadar, RaDICaL, CARRADA, RODNet [ 76 , 77 , 78 , 79 ] is a common approach. In relevant studies, Tim A. Wheeler introduced a modeling method that utilizes a conditional probability distribution model, based on scene information, to predict the radar’s RAmap.…”

Section: Radar Modeling Methods For Autonomous Drivingmentioning

confidence: 99%

An Overview of Millimeter-Wave Radar Modeling Methods for Autonomous Driving Simulation Applications

Huang,

Ding,

Deng

2024

Sensors

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Autonomous driving technology is considered the trend of future transportation. Millimeter-wave radar, with its ability for long-distance detection and all-weather operation, is a key sensor for autonomous driving. The development of various technologies in autonomous driving relies on extensive simulation testing, wherein simulating the output of real radar through radar models plays a crucial role. Currently, there are numerous distinctive radar modeling methods. To facilitate the better application and development of radar modeling methods, this study first analyzes the mechanism of radar detection and the interference factors it faces, to clarify the content of modeling and the key factors influencing modeling quality. Then, based on the actual application requirements, key indicators for measuring radar model performance are proposed. Furthermore, a comprehensive introduction is provided to various radar modeling techniques, along with the principles and relevant research progress. The advantages and disadvantages of these modeling methods are evaluated to determine their characteristics. Lastly, considering the development trends of autonomous driving technology, the future direction of radar modeling techniques is analyzed. Through the above content, this paper provides useful references and assistance for the development and application of radar modeling methods.

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Section: Radar Modeling Methods For Autonomous Drivingmentioning

confidence: 99%

An Overview of Millimeter-Wave Radar Modeling Methods for Autonomous Driving Simulation Applications

Huang,

Ding,

Deng

2024

Sensors

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“…Through the Doppler effect, mmWave radar can also measure the speed of targets, which makes it preferable in object detection and tracking applications. The greatest strength of mmWave radar is that, due to the sensing spectrum, it can work in all weather conditions without losing too much ranging accuracy [76], [77]. Among different technologies, Frequency-Modulated Continuous-Wave (FMCW) radar systems are more appealing as they can provide relatively dense representations of the environment, which is necessary and proved to be effective in navigation and mapping fields [50].…”

Section: B Sensor Configurationmentioning

confidence: 99%

Standard Datasets for Autonomous Navigation and Mapping: A Full-Stack Construction Methodology

Liu,

Fu,

Qin

et al. 2024

IEEE Trans. Intell. Veh.

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The development of intelligent Vehicles (IVs) requires extensive standard datasets for training, benchmarking, and improvement. Autonomous Navigation and Mapping (ANM), as a critical technology for IVs, imposes exceptionally high demands on dataset construction. This is significant in its requirements for comprehensive sensor calibration, precise time synchronization, and accurate generation of ground truth. Besides, the whole construction workflow also demands intricate knowledge and sophisticated practices, necessitating lengthy learning curves for researchers to attain proficiency. The above challenges have led to a slow production of qualified datasets, directly constraining the advancement of ANM. However, so far, an investigation focused on a mature construction methodology of ANM dataset is still missing. This paper strives to fill the gap. Specifically, based on our systematic reviews and extensive practices, for the first time, a full-stack construction methodology of ANM dataset is proposed, including modules of platform construction, sensor calibration, time synchronization, ground truth generation, synthetic data production, and benchmark criteria, with detailed techniques and methodological routes provided in each step. Several long-standing issues are resolved within the methodology. Importantly, we introduce versatile calibration and synchronization frameworks that attain up to us-level and mmlevel precision. Besides, we propose a full-scenario ground truth system that can generate scene-map and trajectory at cm-level accuracy. To verify the effectiveness of our methodology, we design a high-quality dataset and benchmark multiple state-ofthe-art algorithms on it. The successful workflow demonstrates that our methodology can significantly reduce the research threshold and help individuals and institutions to construct datasets in a standardized way.

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“…Besides, to test the generalization of our method, we also conduct experiments on the recently published ColoRadar data set (Kramer et al, 2021). The data set contains repeated scenes in ArpgLab sequences and can be used for relocalization experiments.…”

Section: Experiments and Evaluationmentioning

confidence: 99%

Relocalization based on millimeter wave radar point cloud for visually degraded environments

Cheng

Pang

Jiang³

et al. 2023

Journal of Field Robotics

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Simultaneously localization and mapping (SLAM) has been widely used in autonomous mobile systems to fulfill autonomous navigation. Relocalization plays an important role in SLAM for closing the loop and eliminating the drift of pose estimation. Traditional methods mostly rely on LiDAR or camera sensors, which may degrade or even fail in rainy or dusty situations or with large illumination changes. In this article, we explore the use of low‐cost commercial millimeter wave (mmWave) radars and propose a noval mmWave radar point cloud‐based relocalization method. Our method first pre‐processes the radar point cloud and, based on that, achieves fast 3‐DOF pose estimation for the robot. We build a prototype and thoroughly evaluate our method using data sets collected by our platform in four complex environments, including street, park, road, and water surface scenarios. The experimental results show that our method consistently outperforms other baseline methods including the vision‐based counterparts, especially in the visual degraded scenes.

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ColoRadar: The direct 3D millimeter wave radar dataset

Cited by 39 publications

References 24 publications

An Overview of Millimeter-Wave Radar Modeling Methods for Autonomous Driving Simulation Applications

An Overview of Millimeter-Wave Radar Modeling Methods for Autonomous Driving Simulation Applications

Standard Datasets for Autonomous Navigation and Mapping: A Full-Stack Construction Methodology

Relocalization based on millimeter wave radar point cloud for visually degraded environments

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