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dc.contributor.author
Reijgwart, Victor
dc.contributor.author
Millane, Alexander
dc.contributor.author
Oleynikova, Helen
dc.contributor.author
Siegwart, Roland
dc.contributor.author
Cadena, Cesar
dc.contributor.author
Nieto, Juan
dc.date.accessioned
2019-12-16T07:51:53Z
dc.date.available
2019-12-15T01:50:44Z
dc.date.available
2019-12-16T07:51:53Z
dc.date.issued
2020-01
dc.identifier.issn
2377-3766
dc.identifier.other
10.1109/lra.2019.2953859
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/385682
dc.identifier.doi
10.3929/ethz-b-000385682
dc.description.abstract
Globally consistent dense maps are a key requirement for long-term robot navigation in complex environments. While previous works have addressed the challenges of dense mapping and global consistency, most require more computational resources than may be available on-board small robots. We propose a framework that creates globally consistent volumetric maps on a CPU and is lightweight enough to run on computationally constrained platforms. Our approach represents the environment as a collection of overlapping signed distance function (SDF) submaps and maintains global consistency by computing an optimal alignment of the submap collection. By exploiting the underlying SDF representation, we generate correspondence-free constraints between submap pairs that are computationally efficient enough to optimize the global problem each time a new submap is added. We deploy the proposed system on a hexacopter micro aerial vehicle (MAV) with an Intel i7-8650 U CPU in two realistic scenarios: mapping a large-scale area using a 3D LiDAR and mapping an industrial space using an RGB-D camera. In the large-scale outdoor experiments, the system optimizes a 120 × 80 m map in less than 4 s and produces absolute trajectory RMSEs of less than 1 m over 400 m trajectories. Our complete system, called voxgraph, is available as open source.
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
SLAM
en_US
dc.subject
Aerial Systems
en_US
dc.subject
Perception and Autonomy
en_US
dc.subject
Mapping
en_US
dc.title
Voxgraph: Globally Consistent, Volumetric Mapping Using Signed Distance Function Submaps
en_US
dc.type
Journal Article
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2019-11-18
ethz.journal.title
IEEE Robotics and Automation Letters
ethz.journal.volume
5
en_US
ethz.journal.issue
1
en_US
ethz.pages.start
227
en_US
ethz.pages.end
234
en_US
ethz.size
8 p. accepted version
en_US
ethz.version.deposit
acceptedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
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.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.date.deposited
2019-12-15T01:50:53Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2019-12-16T07:52:11Z
ethz.rosetta.lastUpdated
2022-03-29T00:29:24Z
ethz.rosetta.versionExported
true
ethz.COinS
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