Millimeter Wave FMCW RADARs for Perception, Recognition and Localization in Automotive Applications: A Survey

Venon, Arthur Guillaume; Dupuis, Yohan; Vasseur, Pascal; Merriaux, Pierre

doi:10.1109/tiv.2022.3167733

Cited by 64 publications

(24 citation statements)

References 131 publications

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“…Each green surface point is assigned up to one correspondence per keyframe. Residuals are weighted based on similarity (11) as indicated by grayscale and width: ranging from thin-white (low weight) to thick-black (high weight). Our matching step alternates between assigning correspondences (seen in this figure), and minimizing (15).…”

Section: F Jointly Registering To Multiple Keyframesmentioning

confidence: 99%

“…In contrast, radar is largely resilient to these conditions [2], [9], [10], and receives increasingly higher attention for various perception tasks [11]. Hence, radar has great potential to enable robust localization in a wide range of scenarios; from autonomous cars and logistics where uninterrupted autonomy is required, to work machines surrounded by fugitive dust when operating on unpaved roads and within underground mines or construction sites.…”

Section: Introductionmentioning

confidence: 99%

“…While spinning radar provides accurate and dense data, it has been considered hard to interpret due to its challenging noise characteristics [11]. Consequently, previous radar Ground truth Estimated trajectory 130m Fig.…”

Section: Introductionmentioning

confidence: 99%

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Lidar-Level Localization With Radar? The CFEAR Approach to Accurate, Fast, and Robust Large-Scale Radar Odometry in Diverse Environments

Adolfsson¹,

Magnusson²,

Alhashimi³

et al. 2023

IEEE Trans. Robot.

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This paper presents an accurate, highly efficient, and learning-free method for large-scale odometry estimation using spinning radar, empirically found to generalize well across very diverse environments -outdoors, from urban to woodland, and indoors in warehouses and mines -without changing parameters. Our method integrates motion compensation within a sweep with one-to-many scan registration that minimizes distances between nearby oriented surface points and mitigates outliers with a robust loss function. Extending our previous approach CFEAR, we present an in-depth investigation on a wider range of data sets, quantifying the importance of filtering, resolution, registration cost and loss functions, keyframe history, and motion compensation. We present a new solving strategy and configuration that overcomes previous issues with sparsity and bias, and improves our state-of-the-art by 38%, thus, surprisingly, outperforming radar SLAM and approaching lidar SLAM. The most accurate configuration achieves 1.09% error at 5 Hz on the Oxford benchmark, and the fastest achieves 1.79% error at 160 Hz.

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Section: F Jointly Registering To Multiple Keyframesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Lidar-Level Localization With Radar? The CFEAR Approach to Accurate, Fast, and Robust Large-Scale Radar Odometry in Diverse Environments

Adolfsson¹,

Magnusson²,

Alhashimi³

et al. 2023

IEEE Trans. Robot.

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“…There are also some related surveys that provide an analysis of particular narrow scopes, such as application scenarios [10], [11] and sensing techniques [13], [14]. We summarize these surveys in Table.…”

Section: Related Workmentioning

confidence: 99%

“…There have been some related surveys that provide a summary of particular scopes, such as mmWave-based communication [8], [9], application scenarios [10], [11] and sensing techniques [12]- [14]. We compare and summarize them in detail in the next section.…”

Section: Introductionmentioning

confidence: 99%

A Survey of mmWave-Based Human Sensing: Technology, Platforms and Applications

Zhang,

Xi,

et al. 2023

IEEE Commun. Surv. Tutorials

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With the rapid development of the Internet of Things (IoT) and the rise of 5G communication networks and automatic driving, millimeter wave (mmWave) sensing is emerging and starts impacting our life and workspace. mmWave sensing can sense humans and objects in a contactless way, providing fine-grained sensing ability. In the past few years, many mmWave sensing techniques have been proposed and applied in various human sensing applications (e.g., human localization, gesture recognition, and vital monitoring). We discover the need of a comprehensive survey to summarize the technology, platforms and applications of mmWave-based human sensing. In this survey, we first present the mmWave hardware platforms and some key techniques of mmWave sensing. We then provide a comprehensive review of existing mmWave-based human sensing works. Specifically, we divide existing works into four categories according to the sensing granularity: human tracking and localization, motion recognition, biometric measurement and human imaging. Finally, we discuss the potential research challenges and present future directions in this area.

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Cooperative sensing, communication and computation resource allocation in mobile edge computing-enabled vehicular networks

Li,

Fu,

Tian

et al. 2024

Journal of Information and Intelligence

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Millimeter Wave FMCW RADARs for Perception, Recognition and Localization in Automotive Applications: A Survey

Cited by 64 publications

References 131 publications

Lidar-Level Localization With Radar? The CFEAR Approach to Accurate, Fast, and Robust Large-Scale Radar Odometry in Diverse Environments

Lidar-Level Localization With Radar? The CFEAR Approach to Accurate, Fast, and Robust Large-Scale Radar Odometry in Diverse Environments

A Survey of mmWave-Based Human Sensing: Technology, Platforms and Applications

Cooperative sensing, communication and computation resource allocation in mobile edge computing-enabled vehicular networks

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