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dc.contributor.author
Teixeira, Lucas
dc.contributor.author
Maffra, Fabiola
dc.contributor.author
Moos, Marco
dc.contributor.author
Chli, Margarita
dc.date.accessioned
2018-12-04T15:20:48Z
dc.date.available
2018-05-15T17:09:50Z
dc.date.available
2018-05-16T08:27:44Z
dc.date.available
2018-12-04T15:20:48Z
dc.date.issued
2018-10
dc.identifier.issn
2377-3766
dc.identifier.other
10.1109/LRA.2018.2837687
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/264548
dc.identifier.doi
10.3929/ethz-b-000264548
dc.description.abstract
With a large body of literature dedicated to ego-motion estimation and perception of a robot's workspace, the Robotics community has seen some impressive advances in self-localization and mapping, however, we are still far from general applicability of such approaches in real scenarios. Driven by the need for portable and low-cost solutions to relative pose estimation between Unmanned Aerial Vehicles (UAVs), in this work we propose a new framework to track a master UAV in real-time, carrying a known constellation of LED markers, from a slave UAV without any other pose estimation capability. This setup is especially interesting to aerial manipulation and close-up inspection of structures with low or no texture. Our approach is able to fuse the estimated master's pose with the slave's onboard inertial readings, supporting intermittent communication between the UAVs. Evaluation on both simulation and real indoor and outdoor experiments reveals that the proposed approach achieves unprecedented robustness to noise and occlusion, accuracy and speed of computation. All the code to reproduce this work is publicly available.
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
Aerial Systems: Perception and Autonomy
en_US
dc.subject
Localization
en_US
dc.subject
Visual-Based Navigation
en_US
dc.subject
Sensor fusion
en_US
dc.title
VI-RPE: Visual-Inertial Relative Pose Estimation for Aerial Vehicles
en_US
dc.type
Journal Article
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2018-05-17
ethz.journal.title
IEEE Robotics and Automation Letters
ethz.journal.volume
3
en_US
ethz.journal.issue
4
en_US
ethz.pages.start
2770
en_US
ethz.pages.end
2777
en_US
ethz.version.deposit
submittedVersion
en_US
ethz.grant
Collaborative Aerial Robotic Workers
en_US
ethz.grant
Collaborative vision-based perception for teams of (aerial) robots
en_US
ethz.identifier.scopus
ethz.publication.place
New York, NY
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::09559 - Chli, Margarita (SNF-Professur) / Chli, Margarita (SNF-Professur)
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::09559 - Chli, Margarita (SNF-Professur) / Chli, Margarita (SNF-Professur)
en_US
ethz.grant.agreementno
644128
ethz.grant.agreementno
157585
ethz.grant.fundername
SBFI
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100007352
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
H2020
ethz.grant.program
ethz.date.deposited
2018-05-15T17:09:54Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2018-12-04T15:20:56Z
ethz.rosetta.lastUpdated
2018-12-04T15:20:56Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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