Title:
Modern MAP Inference Methods for Accurate and Fast Occupancy Grid Mapping on Higher Order Factor Graphs
Modern MAP Inference Methods for Accurate and Fast Occupancy Grid Mapping on Higher Order Factor Graphs
| dc.contributor.author | Dhiman, Vikas | |
| dc.contributor.author | Kundu, Abhijit | |
| dc.contributor.author | Dellaert, Frank | |
| dc.contributor.author | Corso, Jason J. | |
| 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.contributor.corporatename | State University of New York at Buffalo. Department of Computer Science and Engineering | en_US |
| dc.date.accessioned | 2015-08-12T20:07:29Z | |
| dc.date.available | 2015-08-12T20:07:29Z | |
| dc.date.issued | 2014 | |
| 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/ICRA.2014.6907129 | |
| dc.description.abstract | Using the inverse sensor model has been popular in occupancy grid mapping. However, it is widely known that applying the inverse sensor model to mapping requires certain assumptions that are not necessarily true. Even the works that use forward sensor models have relied on methods like expectation maximization or Gibbs sampling which have been succeeded by more effective methods of maximum a posteriori (MAP) inference over graphical models. In this paper, we propose the use of modern MAP inference methods along with the forward sensor model. Our implementation and experimental results demonstrate that these modern inference methods deliver more accurate maps more efficiently than previously used methods. | en_US |
| dc.embargo.terms | null | en_US |
| dc.identifier.citation | Dhiman, V.; Kundu, A.; Dellaert, F.; & Corso, J.J. (2014). "Modern MAP Inference Methods for Accurate and Fast Occupancy Grid Mapping on Higher Order Factor Graphs". IEEE International Conference on Robotics and Automation (ICRA 2014), May 31 2014-June 7 2014, pp. 2037-2044. | en_US |
| dc.identifier.doi | 10.1109/ICRA.2014.6907129 | |
| dc.identifier.uri | http://hdl.handle.net/1853/53724 | |
| 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 | Grid mapping | en_US |
| dc.subject | Inverse sensor model | en_US |
| dc.subject | MAP | en_US |
| dc.subject | Maximum a posteriori | en_US |
| dc.title | Modern MAP Inference Methods for Accurate and Fast Occupancy Grid Mapping on Higher Order Factor Graphs | en_US |
| dc.type | Text | |
| dc.type.genre | Proceedings | |
| dspace.entity.type | Publication | |
| local.contributor.author | Dellaert, Frank | |
| local.contributor.corporatename | Institute for Robotics and Intelligent Machines (IRIM) | |
| local.contributor.corporatename | College of Computing | |
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