Unsupervised Place Recognition with Deep Embedding Learning over Radar Videos
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Abstract (arXiv)
We learn, in an unsupervised way, an embedding from sequences of radar images that is suitable for solving place recognition problem using complex radar data. We experiment on 280 km of data and show performance exceeding state-of-the-art supervised approaches, localising correctly 98.38% of the time when using just the nearest database candidate.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 9 references that are in the map. First posted on arXiv 12 Jun 2021.
Builds on
7 of 9 references.
References · 7
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
- 2019Unsupervised Embedding Learning via Invariant and Spreading Instance Featurecited by 231
- 2019The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Datasetcited by 133
- 2020Under the Radar: Learning to Predict Robust Keypoints for Odometry Estimation and Metric Localisation in Radarcited by 17
- 2020Kidnapped Radar: Topological Radar Localisation using Rotationally-Invariant Metric Learningcited by 16
- 2020Look Around You: Sequence-based Radar Place Recognition with Learned Rotational Invariancecited by 14
- 2021RadarLoc: Learning to Relocalize in FMCW Radarcited by 5
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Citing papers per year: 2021: 1.
1 paper.