Title:
SLAM with Object Discovery, Modeling and Mapping
SLAM with Object Discovery, Modeling and Mapping
| dc.contributor.author | Choudhary, Siddharth | |
| dc.contributor.author | Trevor, Alexander J. B. | |
| dc.contributor.author | Christensen, Henrik I. | |
| dc.contributor.author | Dellaert, Frank | |
| dc.contributor.corporatename | Georgia Institute of Technology. Institute for Robotics and Intelligent Machines | en_US |
| dc.contributor.corporatename | Georgia Institute of Technology. College of Computing | en_US |
| dc.contributor.corporatename | Georgia Institute of Technology. School of Interactive Computing | en_US |
| dc.date.accessioned | 2015-08-12T19:38:06Z | |
| dc.date.available | 2015-08-12T19:38:06Z | |
| dc.date.issued | 2014-09 | |
| dc.description | © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. | en_US |
| dc.description | DOI: 10.1109/IROS.2014.6942683 | |
| dc.description.abstract | Object discovery and modeling have been widely studied in the computer vision and robotics communities. SLAM approaches that make use of objects and higher level features have also recently been proposed. Using higher level features provides several benefits: these can be more discriminative, which helps data association, and can serve to inform service robotic tasks that require higher level information, such as object models and poses. We propose an approach for online object discovery and object modeling, and extend a SLAM system to utilize these discovered and modeled objects as landmarks to help localize the robot in an online manner. Such landmarks are particularly useful for detecting loop closures in larger maps. In addition to the map, our system outputs a database of detected object models for use in future SLAM or service robotic tasks. Experimental results are presented to demonstrate the approach’s ability to detect and model objects, as well as to improve SLAM results by detecting loop closures. | en_US |
| dc.embargo.terms | null | en_US |
| dc.identifier.citation | Choudhary, S.; Trevor, A.J.B.; Christensen, H.I.; & Dellaert, F. (2014). "SLAM With Object Discovery, Modeling and Mapping". IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), 14-18 September 2014, pp. 1018-1025. | en_US |
| dc.identifier.doi | 10.1109/IROS.2014.6942683 | |
| dc.identifier.uri | http://hdl.handle.net/1853/53723 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.publisher.original | Institute of Electrical and Electronics Engineers | |
| dc.subject | Loop closure detection | en_US |
| dc.subject | Object discovery | en_US |
| dc.subject | Object modeling | en_US |
| dc.subject | Robotics | en_US |
| dc.subject | Simultaneous localization and mapping | en_US |
| dc.subject | SLAM | en_US |
| dc.title | SLAM with Object Discovery, Modeling and Mapping | en_US |
| dc.type | Text | |
| dc.type.genre | Proceedings | |
| dspace.entity.type | Publication | |
| local.contributor.author | Christensen, Henrik I. | |
| local.contributor.author | Dellaert, Frank | |
| local.contributor.corporatename | Institute for Robotics and Intelligent Machines (IRIM) | |
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| relation.isAuthorOfPublication | dac80074-d9d8-4358-b6eb-397d95bdc868 | |
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