Title:
A Multifrontal QR Factorization Approach to Distributed Inference Applied to Multi-Robot Localization -and Mapping
A Multifrontal QR Factorization Approach to Distributed Inference Applied to Multi-Robot Localization -and Mapping
| dc.contributor.author | Dellaert, Frank | |
| dc.contributor.author | Krauthausen, Peter | |
| dc.contributor.corporatename | Georgia Institute of Technology. Center for Robotics and Intelligent Machines | |
| dc.contributor.corporatename | Georgia Institute of Technology. College of Computing | |
| dc.date.accessioned | 2011-04-15T15:00:37Z | |
| dc.date.available | 2011-04-15T15:00:37Z | |
| dc.date.issued | 2005 | |
| dc.description.abstract | QR factorization is most often used as a black box algorithm, but is in fact an elegant computation on a factor graph. By computing a rooted clique tree on this graph, the computation can be parallelized across subtrees, which forms the basis of so-called multifrontal QR methods. By judiciously choosing the order in which variables are eliminated in the clique tree computation, we show that one straightforwardly obtains a method for performing inference in distributed sensor networks. One obvious application is distributed localization and mapping with a team of robots. We phrase the problem as inference on a large-scale Gaussian Markov Random Field induced by the measurement factor graph, and show how multifrontal QR on this graph solves for the global map and all the robot poses in a distributed fashion. The method is illustrated using both small and large-scale simulations, and validated in practice through actual robot experiments. | en_US |
| dc.identifier.citation | Dellaert, F. and Krauthausen, P. (2005). "A Multifrontal QR Factorization Approach to Distributed Inference Applied to Multi-Robot Localization -and Mapping". Proceedings of the National Conference on Artificial Intelligence (AAAI 2005), pp. 1261–1266. | en_US |
| dc.identifier.uri | http://hdl.handle.net/1853/38543 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.publisher.original | American Association for Artificial Intelligence | |
| dc.title | A Multifrontal QR Factorization Approach to Distributed Inference Applied to Multi-Robot Localization -and Mapping | 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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