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dc.contributor.author
Nubert, Julian
dc.contributor.author
Walther, Etienne
dc.contributor.author
Khattak, Shehryar Masaud Khan
dc.contributor.author
Hutter, Marco
dc.date.accessioned
2023-03-31T10:25:27Z
dc.date.available
2023-03-31T10:25:27Z
dc.date.issued
2022
dc.identifier.isbn
978-1-6654-7927-1
en_US
dc.identifier.isbn
978-1-6654-7928-8
en_US
dc.identifier.issn
2153-0858
dc.identifier.other
10.1109/IROS47612.2022.9982257
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/606000
dc.identifier.doi
10.3929/ethz-b-000558164
dc.description.abstract
LiDAR-based localization and mapping is one of the core components in many modern robotic systems due to the direct integration of range and geometry, allowing for precise motion estimation and generation of high quality maps in real-time. Yet, as a consequence of insufficient environmental constraints present in the scene, this dependence on geometry can result in localization failure, happening in self-symmetric surroundings such as tunnels. This work addresses precisely this issue by proposing a neural network-based estimation approach for detecting (non-)localizability during robot operation. Special attention is given to the localizability of scan-to-scan registration, as it is a crucial component in many LiDAR odometry estimation pipelines. In contrast to previous, mostly traditional detection approaches, the proposed method enables early detection of failure by estimating the localizability on raw sensor measurements without evaluating the underlying registration optimization. Moreover, previous approaches remain limited in their ability to generalize across environments and sensor types, as heuristic-tuning of degeneracy detection thresholds is required. The proposed approach avoids this problem by learning from a corpus of different environments, allowing the network to function over various scenarios. Furthermore, the network is trained exclusively on simulated data, avoiding arduous data collection in challenging and degenerate, often hard-to-access, environments. The presented method is tested during field experiments conducted across challenging environments and on two different sensor types without any modifications. The observed detection performance is on par with state-of-the-art methods after environment-specific threshold tuning.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
Deep Learning in Robotics and Automation
en_US
dc.subject
Localization and Mapping
en_US
dc.subject
Field robotics
en_US
dc.subject
Machine learning (artificial intelligence)
en_US
dc.subject
Mobile robot localization
en_US
dc.title
Learning-based Localizability Estimation for Robust LiDAR Localization
en_US
dc.type
Conference Paper
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2022-12-26
ethz.book.title
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
en_US
ethz.pages.start
17
en_US
ethz.pages.end
24
en_US
ethz.size
8 p. accepted version
en_US
ethz.version.deposit
acceptedVersion
en_US
ethz.event
35th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022)
en_US
ethz.event.location
Kyoto, Japan
en_US
ethz.event.date
October 23-27, 2022
en_US
ethz.grant
Learning Mobility for Real Legged Robots
en_US
ethz.grant
Natural Intelligence for Robotic Monitoring of Habitats
en_US
ethz.grant
Perceptive Dynamic Locomotion on Rough Terrain
en_US
ethz.identifier.wos
ethz.publication.place
Piscataway, NJ
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::02620 - Inst. f. Robotik u. Intelligente Systeme / Inst. Robotics and Intelligent Systems::09570 - Hutter, Marco / Hutter, Marco
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::02620 - Inst. f. Robotik u. Intelligente Systeme / Inst. Robotics and Intelligent Systems::09570 - Hutter, Marco / Hutter, Marco
en_US
ethz.grant.agreementno
852044
ethz.grant.agreementno
101016970
ethz.grant.agreementno
188596
ethz.grant.fundername
EC
ethz.grant.fundername
EC
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100000780
ethz.grant.funderDoi
10.13039/501100000780
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
H2020
ethz.grant.program
H2020
ethz.grant.program
Projekte MINT
ethz.date.deposited
2022-07-14T21:53:44Z
ethz.source
FORM
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2023-03-31T10:25:29Z
ethz.rosetta.lastUpdated
2024-02-02T21:27:20Z
ethz.rosetta.versionExported
true
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/558164
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/603863
ethz.COinS
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