A convex relaxation for approximate global optimization in simultaneous localization and mapping
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...
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Institute of Electrical and Electronics Engineers (IEEE)
2017
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| 在线阅读: | http://hdl.handle.net/1721.1/107496 https://orcid.org/0000-0001-8964-1602 https://orcid.org/0000-0002-8863-6550 |
| _version_ | 1826202410388815872 |
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| author | DuHadway, Charles Rosen, David Matthew Leonard, John J |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory DuHadway, Charles Rosen, David Matthew Leonard, John J |
| author_sort | DuHadway, Charles |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T12:07:00Z |
| format | Article |
| id | mit-1721.1/107496 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T12:07:00Z |
| publishDate | 2017 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1074962022-09-28T00:15:50Z A convex relaxation for approximate global optimization in simultaneous localization and mapping DuHadway, Charles Rosen, David Matthew Leonard, John J Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Mechanical Engineering Rosen, David Matthew Leonard, John J 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. Google (Firm) (Software Engineering Internship) United States. Office of Naval Research (Grants N00014-10-1-0936, N00014-11-1-0688 and N00014- 13-1-0588) National Science Foundation (U.S.) (Award IIS-1318392) 2017-03-20T15:27:44Z 2017-03-20T15:27:44Z 2015-07 2015-05 Article http://purl.org/eprint/type/ConferencePaper 978-1-4799-6923-4 http://hdl.handle.net/1721.1/107496 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. https://orcid.org/0000-0001-8964-1602 https://orcid.org/0000-0002-8863-6550 en_US http://dx.doi.org/10.1109/ICRA.2015.7140014 Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) MIT Web Domain |
| spellingShingle | DuHadway, Charles Rosen, David Matthew Leonard, John J A convex relaxation for approximate global optimization in simultaneous localization and mapping |
| title | A convex relaxation for approximate global optimization in simultaneous localization and mapping |
| title_full | A convex relaxation for approximate global optimization in simultaneous localization and mapping |
| title_fullStr | A convex relaxation for approximate global optimization in simultaneous localization and mapping |
| title_full_unstemmed | A convex relaxation for approximate global optimization in simultaneous localization and mapping |
| title_short | A convex relaxation for approximate global optimization in simultaneous localization and mapping |
| title_sort | convex relaxation for approximate global optimization in simultaneous localization and mapping |
| url | http://hdl.handle.net/1721.1/107496 https://orcid.org/0000-0001-8964-1602 https://orcid.org/0000-0002-8863-6550 |
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