Difference between revisions of "Publications:Sensor Based Adaptive Metric-Topological Cell Decomposition Method for Semantic Annotation of 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=750189 | |PID=750189 | ||
| − | |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=Sensor Based Adaptive Metric-Topological Cell Decomposition Method for Semantic Annotation of Structured Environments | |Title=Sensor Based Adaptive Metric-Topological Cell Decomposition Method for Semantic Annotation of Structured Environments | ||
|PublicationType=Conference Paper | |PublicationType=Conference Paper | ||
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|Volume= | |Volume= | ||
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| − | |HostPublication= | + | |HostPublication=2014 13th International Conference on Control Automation Robotics & Vision (ICARCV) |
|Conference=13th International Conference on Control, Automation, Robotics and Vision, ICARCV 2014, Marina Bay Sands, Singapore, December 10-12, 2014 | |Conference=13th International Conference on Control, Automation, Robotics and Vision, ICARCV 2014, Marina Bay Sands, Singapore, December 10-12, 2014 | ||
| − | |StartPage= | + | |StartPage=1771 |
| − | |EndPage= | + | |EndPage=1777 |
|Year=2014 | |Year=2014 | ||
|Edition= | |Edition= | ||
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| − | |City= | + | |City=Piscataway, NJ |
| − | |Publisher= | + | |Publisher=IEEE Press |
|Series= | |Series= | ||
|SeriesISSN= | |SeriesISSN= | ||
| − | |ISBN= | + | |ISBN=978-147995199-4 |
|Urls= | |Urls= | ||
|ISRN= | |ISRN= | ||
| − | |DOI= | + | |DOI=http://dx.doi.org/10.1109/ICARCV.2014.7064584 |
|ISI= | |ISI= | ||
|PMID= | |PMID= | ||
| − | |ScopusId= | + | |ScopusId=2-s2.0-84946687398 |
|NBN=urn:nbn:se:hh:diva-26597 | |NBN=urn:nbn:se:hh:diva-26597 | ||
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| − | |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. Article number: 7064584</p> |
| − | |Abstract=<p>A fundamental ingredient for semantic labeling is a reliable method for determining and representing the relevant spatial features of an environment. We address this challenge for planar metric-topological maps based on occupancy grids. Our method detects arbitrary dominant orientations in the presence of significant clutter, fits corresponding line features with tunable resolution, and extracts topological information by polygonal cell decomposition. Real-world case studies taken from the target application domain (autonomous forklift trucks in warehouses) demonstrate the performance and robustness of our method, while results from a preliminary algorithm to extract corridors, and junctions, demonstrate its expressiveness. Contribution of this work starts with the formulation of metric-topological surveying of environment, and a generic n-direction planar representation accompanied with a general method for extracting it from occupancy map. The implementation also includes some semantic labels specific to warehouse like environments.</p> | + | |Abstract=<p>A fundamental ingredient for semantic labeling is a reliable method for determining and representing the relevant spatial features of an environment. We address this challenge for planar metric-topological maps based on occupancy grids. Our method detects arbitrary dominant orientations in the presence of significant clutter, fits corresponding line features with tunable resolution, and extracts topological information by polygonal cell decomposition. Real-world case studies taken from the target application domain (autonomous forklift trucks in warehouses) demonstrate the performance and robustness of our method, while results from a preliminary algorithm to extract corridors, and junctions, demonstrate its expressiveness. Contribution of this work starts with the formulation of metric-topological surveying of environment, and a generic n-direction planar representation accompanied with a general method for extracting it from occupancy map. The implementation also includes some semantic labels specific to warehouse like environments. © 2014 IEEE.</p> |
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|CreatedDate=2014-09-26 | |CreatedDate=2014-09-26 | ||
|PublicationDate=2014-09-26 | |PublicationDate=2014-09-26 | ||
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|diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:750189}} | |diva=http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:750189}} | ||
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| Title | Sensor Based Adaptive Metric-Topological Cell Decomposition Method for Semantic Annotation of Structured Environments |
|---|---|
| Author | |
| Year | 2014 |
| PublicationType | Conference Paper |
| Journal | |
| HostPublication | 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV) |
| Conference | 13th International Conference on Control, Automation, Robotics and Vision, ICARCV 2014, Marina Bay Sands, Singapore, December 10-12, 2014 |
| DOI | http://dx.doi.org/10.1109/ICARCV.2014.7064584 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:750189 |
| Abstract | A fundamental ingredient for semantic labeling is a reliable method for determining and representing the relevant spatial features of an environment. We address this challenge for planar metric-topological maps based on occupancy grids. Our method detects arbitrary dominant orientations in the presence of significant clutter, fits corresponding line features with tunable resolution, and extracts topological information by polygonal cell decomposition. Real-world case studies taken from the target application domain (autonomous forklift trucks in warehouses) demonstrate the performance and robustness of our method, while results from a preliminary algorithm to extract corridors, and junctions, demonstrate its expressiveness. Contribution of this work starts with the formulation of metric-topological surveying of environment, and a generic n-direction planar representation accompanied with a general method for extracting it from occupancy map. The implementation also includes some semantic labels specific to warehouse like environments. © 2014 IEEE. |
