Show simple item record

dc.contributor.author
Tschopp, Florian
dc.contributor.author
Nieto, Juan
dc.contributor.author
Siegwart, Roland
dc.contributor.author
Cadena, Cesar
dc.date.accessioned
2021-05-31T09:02:58Z
dc.date.available
2021-05-31T08:25:46Z
dc.date.available
2021-05-31T09:02:58Z
dc.date.issued
2021-05-30
dc.identifier.uri
http://hdl.handle.net/20.500.11850/487527
dc.identifier.doi
10.3929/ethz-b-000487527
dc.description.abstract
Introducing semantically meaningful objects to visual Simultaneous Localization and Mapping (SLAM) has the potential to improve both the accuracy and reliability of pose estimates, especially in challenging scenarios with significant viewpoint and appearance changes. However, how semantic objects should be represented for an efficient inclusion in optimization-based SLAM frameworks is still an open question. Superquadrics (SQs) are an efficient and compact object representation, able to represent most common object types to a high degree, and typically retrieved from 3D point-cloud data. However, accurate 3D point-cloud data might not be available in all applications. Recent advancements in machine learning enabled robust object recognition and semantic mask measurements from camera images under many different appearance conditions. We propose a pipeline to leverage such semantic mask measurements to fit SQ parameters to multi-view camera observations using a multi-stage initialization and optimization procedure. We demonstrate the system's ability to retrieve randomly generated SQ parameters from multi-view mask observations in preliminary simulation experiments and evaluate different initialization stages and cost functions.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
ETH Zurich, Autonomous System Lab
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.title
Superquadric Object Representation for Optimization-based Semantic SLAM
en_US
dc.type
Working Paper
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2021-05-31
ethz.size
8 p.
en_US
ethz.publication.place
Zurich
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::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02261 - Center for Sustainable Future Mobility / Center for Sustainable Future Mobility
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
2021-05-31T08:25:51Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-05-31T09:03:05Z
ethz.rosetta.lastUpdated
2022-03-29T08:21:24Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.atitle=Superquadric%20Object%20Representation%20for%20Optimization-based%20Semantic%20SLAM&rft.date=2021-05-30&rft.au=Tschopp,%20Florian&Nieto,%20Juan&Siegwart,%20Roland&Cadena,%20Cesar&rft.genre=preprint&rft.btitle=Superquadric%20Object%20Representation%20for%20Optimization-based%20Semantic%20SLAM
 Search print copy at ETH Library

Files in this item

Thumbnail

Publication type

Show simple item record