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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Main Authors: DuHadway, Charles, Rosen, David Matthew, Leonard, John J
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Format: Article
Language:en_US
Published: Institute of Electrical and Electronics Engineers (IEEE) 2017
Online Access: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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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.
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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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