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dc.contributor.author
Schoppmann, Pascal
dc.contributor.author
Proença, Pedro F.
dc.contributor.author
Delaune, Jeff
dc.contributor.author
Pantic, Michael
dc.contributor.author
Hinzmann, Timo
dc.contributor.author
Matthies, Larry
dc.contributor.author
Siegwart, Roland
dc.contributor.author
Brockers, Roland
dc.date.accessioned
2022-02-03T13:25:56Z
dc.date.available
2022-02-03T12:46:33Z
dc.date.available
2022-02-03T13:02:50Z
dc.date.available
2022-02-03T13:20:47Z
dc.date.available
2022-02-03T13:25:56Z
dc.date.issued
2021
dc.identifier.isbn
978-1-6654-1714-3
en_US
dc.identifier.isbn
978-1-6654-1715-0
en_US
dc.identifier.other
10.1109/iros51168.2021.9636507
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/530706
dc.identifier.doi
10.3929/ethz-b-000530706
dc.description.abstract
In this paper, we propose a resource-efficient approach to provide an autonomous UAV with an on-board perception method to detect safe, hazard-free landing sites during flights over complex 3D terrain. We aggregate 3D measurements acquired from a sequence of monocular images by a Structure-from-Motion approach into a local, robot-centric, multi-resolution elevation map of the overflown terrain, which fuses depth measurements according to their lateral surface resolution (pixel-footprint) in a probabilistic framework based on the concept of dynamic Level of Detail. Map aggregation only requires depth maps and the associated poses, which are obtained from an on-board Visual Odometry algorithm. An efficient landing site detection method then exploits the features of the underlying multi-resolution map to detect safe landing sites based on slope, roughness, and quality of the reconstructed terrain surface. The evaluation of the performance of the mapping and landing site detection modules are analyzed independently and jointly in simulated and real-world experiments in order to establish the efficacy of the proposed approach.
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
Surface reconstruction
en_US
dc.subject
Extraterrestrial measurements
en_US
dc.subject
Probabilistic logic
en_US
dc.subject
3D Mapping
en_US
dc.title
Multi-Resolution Elevation Mapping and Safe Landing Site Detection with Applications to Planetary Rotorcraft
en_US
dc.type
Conference Paper
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2021-12-16
ethz.book.title
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
en_US
ethz.pages.start
1990
en_US
ethz.pages.end
1997
en_US
ethz.version.deposit
acceptedVersion
en_US
ethz.code.ddc
DDC - DDC::0 - Computer science, information & general works::004 - Data processing, computer science
en_US
ethz.event
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021)
en_US
ethz.event.location
Online
en_US
ethz.event.date
September 27 - October 1, 2021
en_US
ethz.grant
NCCR Digital Fabrication
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::03737 - Siegwart, Roland Y. / Siegwart, Roland Y.
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02100 - Dep. Architektur / Dep. of Architecture::02284 - NFS Digitale Fabrikation / NCCR Digital Fabrication
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::03737 - Siegwart, Roland Y. / Siegwart, Roland Y.
en_US
ethz.grant.agreementno
--
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
NCCR (NFS)
ethz.date.deposited
2022-02-03T12:46:39Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2022-02-03T13:02:56Z
ethz.rosetta.lastUpdated
2022-03-29T18:38:48Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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