Contrastive Learning for Unsupervised Radar Place Recognition

Gadd, Matthew; Martini, Daniele De; Newman, Paul

doi:10.1109/icar53236.2021.9659335

Cited by 13 publications

(4 citation statements)

References 31 publications

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“…In a subsequent work, Gadd et al [95,117] focused on unsupervised radar place recognition, employing an embedding learning approach to ensure that features of distinct instances are separated while features of augmented instances remain invariant. Using a VGG-19 as a frontend backbone, they extract local features that should be similar regardless of total augmentation.…”

Section: Radar Based Loop Closurementioning

confidence: 99%

Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization

Brune,

Meisen,

Pomp

2024

Applied Sciences

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This paper provides an in-depth review of deep learning techniques to address the challenges of odometry and global ego-localization using frequency modulated continuous wave (FMCW) radar sensors. In particular, we focus on the prediction of odometry, which involves the determination of the ego-motion of a system by external sensors, and loop closure detection, which concentrates on the determination of the ego-position typically on an existing map. We initially emphasize the significance of these tasks in the context of radar sensors and underscore the motivations behind them. The subsequent sections delve into the practical implementation of deep learning approaches, strategically designed to effectively address the aforementioned challenges. We primarily focus on spinning and automotive radar configurations within the domain of autonomous driving. Additionally, we introduce publicly available datasets that have been instrumental in addressing these challenges and analyze the importance and struggles of current methods used for radar based odometry and localization. In conclusion, this paper highlights the distinctions between the addressed tasks and other radar perception applications, while also discussing their differences from challenges posed by alternative sensor modalities. The findings contribute to the ongoing discourse on advancing radar sensor capabilities through the application of deep learning methodologies, particularly in the context of enhancing odometry and ego-localization for autonomous driving applications.

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Section: Radar Based Loop Closurementioning

confidence: 99%

Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization

Brune,

Meisen,

Pomp

2024

Applied Sciences

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“…Though the vision‐based relocalization achieves good performance in some scenes, in visual degraded scenes such as the night or scenes with large illumination changes, the application of vision‐based relocalization is still limited. To tackle this problem, the usage of the radar sensor in harsh environments has been proposed (Gadd et al, 2021; Wang et al, 2021). They utilize the FMCW scanning radar for direct 6‐DOF pose regression and achieve good performance.…”

Section: Related Workmentioning

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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“…FMCW scanning radar has seen an increasing interest in the past years, from odometry and localisation [5], [6], [7], [2] to semantic segmentation [8], [9], detection [4], path planning [10], [11] and cross-modal localisation [12], [13]. As this work discusses odometry, we will focus on this topic.…”

Section: Related Workmentioning

confidence: 99%

Doppler-Aware Odometry from FMCW Scanning Radar

Rennie,

Williams,

Newman

et al. 2023

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

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This work explores Doppler information from a millimetre-Wave (mm-W) Frequency-Modulated Continuous-Wave (FMCW) scanning radar to make odometry estimation more robust and accurate. Firstly, doppler information is added to the scan masking process to enhance correlative scan matching. Secondly, we train a Neural Network (NN) for regressing forward velocity directly from a single radar scan; we fuse this estimate with the correlative scan matching estimate and show improved robustness to bad estimates caused by challenging environment geometries, e.g. narrow tunnels. We test our method with a novel custom dataset which is released with this work at https://ori.ox.ac.uk/publications/datasets.

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Contrastive Learning for Unsupervised Radar Place Recognition

Cited by 13 publications

References 31 publications

Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization

Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization

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

Doppler-Aware Odometry from FMCW Scanning Radar

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