Radar-on-Lidar: metric radar localization on prior lidar maps

Yin, Huan; Wang, Yue; Tang, Li; Xiong, Rong

doi:10.1109/rcar49640.2020.9303291

Cited by 20 publications

(22 citation statements)

References 17 publications

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“…The top row shows the radar occupancy prediction training process. The middle and bottom rows illustrate the main issues of radar-tolidar training and show the comparison between our and previous works's results [5], [6]. The middle row also shows the proposed method can successfully translate radar to lidar while preserving long-range sensing.…”

Section: Pou-chun Kung Chieh-chih Wang and Wen-chieh Linmentioning

confidence: 72%

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Radar Occupancy Prediction with Lidar Supervision while Preserving Long-Range Sensing and Penetrating Capabilities

Kung¹,

Wang²,

Lin³

2021

Preprint

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Section: Pou-chun Kung Chieh-chih Wang and Wen-chieh Linmentioning

confidence: 72%

“…Rob et al [4] first proposed the radar inverse sensor modeling using lidar as the training ground truth. The radar-to-lidar training achieved by generative adversarial network (GAN) [16] is proposed in [6]. The idea has been explored to height estimation in [5].…”

Section: Related Workmentioning

confidence: 99%

Radar Occupancy Prediction with Lidar Supervision while Preserving Long-Range Sensing and Penetrating Capabilities

Kung¹,

Wang²,

Lin³

2021

Preprint

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“…More efforts explore cross-modality solutions. Yin et al [16] combine the radar and LiDAR outputs, transferring raw radar scans to synthetic LiDAR images and adopting the Monte Carlo algorithm to localize the vehicle. Tang et al [3] propose an unsupervised-learning framework that aligns 2D radar scans with the geometric structure in satellite images.…”

Section: B Radar-based Metric Localizationmentioning

confidence: 99%

DC-Loc: Accurate Automotive Radar Based Metric Localization with Explicit Doppler Compensation

Gao¹,

Zhang²,

Wang³

et al. 2021

Preprint

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Automotive mmWave radar has been widely used in the automotive industry due to its small size, low cost, and complementary advantages to optical sensors (e.g., cameras, LiDAR, etc.) in adverse weathers, e.g., fog, raining, and snowing. On the other side, its large wavelength also poses fundamental challenges to perceive the environment. Recent advances have made breakthroughs on its inherent drawbacks, i.e., the multipath reflection and the sparsity of mmWave radar's point clouds. However, the frequency-modulated continuous wave modulation of radar signals makes it more sensitive to vehicles' mobility than optical sensors. This work focuses on the problem of frequency shift, i.e., the Doppler effect distorts the radar ranging measurements and its knock-on effect on metric localization. We propose a new radar-based metric localization framework that obtains more accurate location estimation by restoring the Doppler distortion. Specifically, we first design a new algorithm that explicitly compensates the Doppler distortion of radar scans and then model the measurement uncertainty of the Doppler-compensated point cloud to further optimize the metric localization. Extensive experiments using the public nuScenes dataset and CARLA simulator demonstrate that our method outperforms the stateof-the-art approach by 25.2% and 5.6% improvements in terms of translation and rotation errors, respectively.

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“…LiDAR localization within LiDAR maps (Ding et al, 2020;Egger et al, 2018;Fu et al, 2018;Levinson & Thrun, 2010;Wolcott & Eustice, 2017), visual localization within visual maps (Murartal & Tardos, 2017;Wong et al, 2017), visual localization within LiDAR maps (Wolcott & Eustice, 2014;Zuo et al, 2020), radar localization within radar maps (Saftescu et al, 2020) and radar localization within LiDAR maps (Yin et al, 2020). Among these approaches, LiDAR localization within LiDAR maps is usually considered to be the most accurate one (Kuutti et al, 2018), and is the focus of this paper.…”

Section: Introductionmentioning

confidence: 99%

LiDAR‐based robust localization for field autonomous vehicles in off‐road environments

Ren

Xue

et al. 2021

Journal of Field Robotics

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Robust localization is an essential capability for autonomous land vehicles. While a lot of work focused on structured environments, this article focuses on navigation in off-road environments. In the off-road environment, due to the lack of salient features, scan matching algorithms tend to degenerate. Therefore, the first contribution of this paper is to propose a reliable degeneracy indicator which can evaluate the scan matching performance. The evaluated degeneracy indicator is then integrated into the factor graph optimization framework which is used in both the offline mapping system and the online localization system. Moreover, a complete navigation system that can handle the incomplete and partly outdated LiDAR maps is developed. Extensive tests on real-world data sets show that the proposed system outperforms state-of-the-art approaches, especially in degenerate scenarios.

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Radar-on-Lidar: metric radar localization on prior lidar maps

Cited by 20 publications

References 17 publications

Radar Occupancy Prediction with Lidar Supervision while Preserving Long-Range Sensing and Penetrating Capabilities

Radar Occupancy Prediction with Lidar Supervision while Preserving Long-Range Sensing and Penetrating Capabilities

DC-Loc: Accurate Automotive Radar Based Metric Localization with Explicit Doppler Compensation

LiDAR‐based robust localization for field autonomous vehicles in off‐road environments

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