Difference between revisions of "Publications:Semi-Supervised Semantic Labeling of Adaptive Cell Decomposition Maps in Well-Structured Environments"
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{{PublicationSetupTemplate|Author=Saeed Gholami Shahbandi, Björn Åstrand, Roland Philippsen | {{PublicationSetupTemplate|Author=Saeed Gholami Shahbandi, Björn Åstrand, Roland Philippsen | ||
|PID=850141 | |PID=850141 | ||
| − | |Name=Gholami Shahbandi, Saeed | + | |Name=Gholami Shahbandi, Saeed (saesha) (0000-0003-3498-0783) (Högskolan i Halmstad (2804), Akademin för informationsteknologi (16904), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), CAISR Centrum för tillämpade intelligenta system (IS-lab) (13650));Åstrand, Björn (bjorn) (Högskolan i Halmstad (2804), Akademin för informationsteknologi (16904), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), CAISR Centrum för tillämpade intelligenta system (IS-lab) (13650));Philippsen, Roland (rolphi) (0000-0003-3513-8854) (Högskolan i Halmstad (2804), Akademin för informationsteknologi (16904), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), CAISR Centrum för tillämpade intelligenta system (IS-lab) (13650)) |
|Title=Semi-Supervised Semantic Labeling of Adaptive Cell Decomposition Maps in Well-Structured Environments | |Title=Semi-Supervised Semantic Labeling of Adaptive Cell Decomposition Maps in Well-Structured Environments | ||
|PublicationType=Conference Paper | |PublicationType=Conference Paper | ||
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|Volume= | |Volume= | ||
|Issue= | |Issue= | ||
| − | |HostPublication= | + | |HostPublication=2015 European Conference on Mobile Robots (ECMR) |
| − | |Conference=European Conference on Mobile Robots 2015, Lincoln, United Kingdom, 2-4 September, 2015 | + | |Conference=7th European Conference on Mobile Robots 2015, Lincoln, United Kingdom, 2-4 September, 2015 |
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| − | |City= | + | |City=Piscataway, NJ |
| − | |Publisher= | + | |Publisher=IEEE Press |
|Series= | |Series= | ||
|SeriesISSN= | |SeriesISSN= | ||
| − | |ISBN= | + | |ISBN=978-1-4673-9163-4;978-1-4673-9163-15 |
|Urls= | |Urls= | ||
|ISRN= | |ISRN= | ||
| − | |DOI= | + | |DOI=http://dx.doi.org/10.1109/ECMR.2015.7324207 |
|ISI= | |ISI= | ||
|PMID= | |PMID= | ||
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| − | |Keywords= | + | |Keywords=Continuous wavelet transforms;Feature extraction;Labeling;Robot sensing systems;Robustness;Semantics |
|Categories=Robotteknik och automation (20201) | |Categories=Robotteknik och automation (20201) | ||
|ResearchSubjects= | |ResearchSubjects= | ||
|Projects=AIMS | |Projects=AIMS | ||
|Notes=<p>This work was supported by the Swedish Knowledge Foundation and industry partners Kollmorgen, Optronic, and Toyota Material Handling Europe.</p> | |Notes=<p>This work was supported by the Swedish Knowledge Foundation and industry partners Kollmorgen, Optronic, and Toyota Material Handling Europe.</p> | ||
| − | |Abstract=<p>We present a semi-supervised approach for semantic mapping, by introducing human knowledge after unsupervised place categorization has been combined with an adaptive cell decomposition of an occupancy map. Place categorization is based on clustering features extracted from raycasting in the occupancy map. The cell decomposition is provided by work we published previously, which is effective for the maps that could be abstracted by straight lines. Compared to related methods, our approach obviates the need for a low-level link between human knowledge and the perception and mapping sub-system, or the onerous preparation of training data for supervised learning. Application scenarios include intelligent warehouse robots which need a heightened awareness in order to operate with a higher degree of autonomy and flexibility, and integrate more fully with inventory management systems. The approach is shown to be robust and flexible with respect to different types of environments and sensor setups.</p> | + | |Abstract=<p>We present a semi-supervised approach for semantic mapping, by introducing human knowledge after unsupervised place categorization has been combined with an adaptive cell decomposition of an occupancy map. Place categorization is based on clustering features extracted from raycasting in the occupancy map. The cell decomposition is provided by work we published previously, which is effective for the maps that could be abstracted by straight lines. Compared to related methods, our approach obviates the need for a low-level link between human knowledge and the perception and mapping sub-system, or the onerous preparation of training data for supervised learning. Application scenarios include intelligent warehouse robots which need a heightened awareness in order to operate with a higher degree of autonomy and flexibility, and integrate more fully with inventory management systems. The approach is shown to be robust and flexible with respect to different types of environments and sensor setups. © 2015 IEEE</p> |
|Opponents= | |Opponents= | ||
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|CreatedDate=2015-09-01 | |CreatedDate=2015-09-01 | ||
|PublicationDate=2015-09-01 | |PublicationDate=2015-09-01 | ||
| − | |LastUpdated=2015- | + | |LastUpdated=2015-12-09 |
|diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:850141}} | |diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:850141}} | ||
<div style='display: none'> | <div style='display: none'> | ||
Latest revision as of 21:41, 30 September 2016
| Title | Semi-Supervised Semantic Labeling of Adaptive Cell Decomposition Maps in Well-Structured Environments |
|---|---|
| Author | Saeed Gholami Shahbandi and Björn Åstrand and Roland Philippsen |
| Year | 2015 |
| PublicationType | Conference Paper |
| Journal | |
| HostPublication | 2015 European Conference on Mobile Robots (ECMR) |
| Conference | 7th European Conference on Mobile Robots 2015, Lincoln, United Kingdom, 2-4 September, 2015 |
| DOI | http://dx.doi.org/10.1109/ECMR.2015.7324207 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:850141 |
| Abstract | We present a semi-supervised approach for semantic mapping, by introducing human knowledge after unsupervised place categorization has been combined with an adaptive cell decomposition of an occupancy map. Place categorization is based on clustering features extracted from raycasting in the occupancy map. The cell decomposition is provided by work we published previously, which is effective for the maps that could be abstracted by straight lines. Compared to related methods, our approach obviates the need for a low-level link between human knowledge and the perception and mapping sub-system, or the onerous preparation of training data for supervised learning. Application scenarios include intelligent warehouse robots which need a heightened awareness in order to operate with a higher degree of autonomy and flexibility, and integrate more fully with inventory management systems. The approach is shown to be robust and flexible with respect to different types of environments and sensor setups. © 2015 IEEE |
