Introspective Loop Closure for SLAM with 4D Imaging Radar Article Swipe
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· 2025
· Open Access
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Simultaneous Localization and Mapping (SLAM) allows mobile robots to navigate without external positioning systems or pre-existing maps. Radar is emerging as a valuable sensing tool, especially in vision-obstructed environments, as it is less affected by particles than lidars or cameras. Modern 4D imaging radars provide three-dimensional geometric information and relative velocity measurements, but they bring challenges, such as a small field of view and sparse, noisy point clouds. Detecting loop closures in SLAM is critical for reducing trajectory drift and maintaining map accuracy. However, the directional nature of 4D radar data makes identifying loop closures, especially from reverse viewpoints, difficult due to limited scan overlap. This article explores using 4D radar for loop closure in SLAM, focusing on similar and opposing viewpoints. We generate submaps for a denser environment representation and use introspective measures to reject false detections in feature-degenerate environments. Our experiments show accurate loop closure detection in geometrically diverse settings for both similar and opposing viewpoints, improving trajectory estimation with up to 82 % improvement in ATE and rejecting false positives in self-similar environments.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- http://arxiv.org/abs/2503.02383
- https://arxiv.org/pdf/2503.02383
- OA Status
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- OpenAlex ID
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Raw OpenAlex JSON
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Introspective Loop Closure for SLAM with 4D Imaging RadarWork title
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articleOpenAlex work type
- Language
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enPrimary language
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2025Year of publication
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2025-03-04Full publication date if available
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Maximilian Hilger, Vladimír Kubelka, Daniel Adolfsson, Ralf Becker, Henrik Andreasson, Achim J. LilienthalList of authors in order
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https://arxiv.org/abs/2503.02383Publisher landing page
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greenOpen access status per OpenAlex
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0Total citation count in OpenAlex
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