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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Detalhes bibliográficos
Principais autores: DuHadway, Charles, Rosen, David Matthew, Leonard, John J
Outros Autores: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Formato: Artigo
Idioma:en_US
Publicado em: Institute of Electrical and Electronics Engineers (IEEE) 2017
Acesso em linha: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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