dc.contributor.author | DuHadway, Charles | |
dc.contributor.author | Rosen, David Matthew | |
dc.contributor.author | Leonard, John J | |
dc.date.accessioned | 2017-03-20T15:27:44Z | |
dc.date.available | 2017-03-20T15:27:44Z | |
dc.date.issued | 2015-07 | |
dc.date.submitted | 2015-05 | |
dc.identifier.isbn | 978-1-4799-6923-4 | |
dc.identifier.uri | http://hdl.handle.net/1721.1/107496 | |
dc.description.abstract | Modern approaches to simultaneous localization and mapping (SLAM) formulate the inference problem as a high-dimensional but sparse nonconvex M-estimation, and then apply general first- or second-order smooth optimization methods to recover a local minimizer of the objective function. The performance of any such approach depends crucially upon initializing the optimization algorithm near a good solution for the inference problem, a condition that is often difficult or impossible to guarantee in practice. To address this limitation, in this paper we present a formulation of the SLAM M-estimation with the property that, by expanding the feasible set of the estimation program, we obtain a convex relaxation whose solution approximates the globally optimal solution of the SLAM inference problem and can be recovered using a smooth optimization method initialized at any feasible point. Our formulation thus provides a means to obtain a high-quality solution to the SLAM problem without requiring high-quality initialization. | en_US |
dc.description.sponsorship | Google (Firm) (Software Engineering Internship) | en_US |
dc.description.sponsorship | United States. Office of Naval Research (Grants N00014-10-1-0936, N00014-11-1-0688 and N00014- 13-1-0588) | en_US |
dc.description.sponsorship | National Science Foundation (U.S.) (Award IIS-1318392) | en_US |
dc.language.iso | en_US | |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1109/ICRA.2015.7140014 | en_US |
dc.rights | Creative Commons Attribution-Noncommercial-Share Alike | en_US |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | en_US |
dc.source | MIT Web Domain | en_US |
dc.title | A convex relaxation for approximate global optimization in simultaneous localization and mapping | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Rosen, David M., Charles DuHadway, and John J. Leonard. “A Convex Relaxation for Approximate Global Optimization in Simultaneous Localization and Mapping.” IEEE, 2015. 5822–5829. | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Mechanical Engineering | en_US |
dc.contributor.mitauthor | Rosen, David Matthew | |
dc.contributor.mitauthor | Leonard, John J | |
dc.relation.journal | Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA) | en_US |
dc.eprint.version | Author's final manuscript | en_US |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | en_US |
eprint.status | http://purl.org/eprint/status/NonPeerReviewed | en_US |
dspace.orderedauthors | Rosen, David M.; DuHadway, Charles; Leonard, John J. | en_US |
dspace.embargo.terms | N | en_US |
dc.identifier.orcid | https://orcid.org/0000-0001-8964-1602 | |
dc.identifier.orcid | https://orcid.org/0000-0002-8863-6550 | |
mit.license | OPEN_ACCESS_POLICY | en_US |