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dc.contributor.authorKaess, Michael
dc.contributor.authorRosen, David Matthew
dc.contributor.authorLeonard, John Joseph
dc.date.accessioned2015-06-30T14:28:14Z
dc.date.available2015-06-30T14:28:14Z
dc.date.issued2014-09
dc.date.submitted2014-01
dc.identifier.issn1552-3098
dc.identifier.issn1941-0468
dc.identifier.urihttp://hdl.handle.net/1721.1/97576
dc.description.abstractMany point estimation problems in robotics, computer vision, and machine learning can be formulated as instances of the general problem of minimizing a sparse nonlinear sum-of-squares objective function. For inference problems of this type, each input datum gives rise to a summand in the objective function, and therefore performing online inference corresponds to solving a sequence of sparse nonlinear least-squares minimization problems in which additional summands are added to the objective function over time. In this paper, we present Robust Incremental least-Squares Estimation (RISE), an incrementalized version of the Powell's Dog-Leg numerical optimization method suitable for use in online sequential sparse least-squares minimization. As a trust-region method, RISE is naturally robust to objective function nonlinearity and numerical ill-conditioning and is provably globally convergent for a broad class of inferential cost functions (twice-continuously differentiable functions with bounded sublevel sets). Consequently, RISE maintains the speed of current state-of-the-art online sparse least-squares methods while providing superior reliability.en_US
dc.description.sponsorshipUnited States. Office of Naval Research (Grant N00014-12-1-0093)en_US
dc.description.sponsorshipUnited States. Office of Naval Research (Grant N00014-11-1-0688)en_US
dc.description.sponsorshipUnited States. Office of Naval Research (Grant N00014-06-1-0043)en_US
dc.description.sponsorshipUnited States. Office of Naval Research (Grant N00014-10-1-0936)en_US
dc.description.sponsorshipUnited States. Air Force Research Laboratory (Contract FA8650-11-C-7137)en_US
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/tro.2014.2321852en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceOther univ. web domainen_US
dc.titleRISE: An Incremental Trust-Region Method for Robust Online Sparse Least-Squares Estimationen_US
dc.typeArticleen_US
dc.identifier.citationRosen, David M., Michael Kaess, and John J. Leonard. “RISE: An Incremental Trust-Region Method for Robust Online Sparse Least-Squares Estimation.” IEEE Trans. Robot. 30, no. 5 (October 2014): 1091–1108.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineeringen_US
dc.contributor.mitauthorRosen, David Matthewen_US
dc.contributor.mitauthorLeonard, John Josephen_US
dc.relation.journalIEEE Transactions on Roboticsen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsRosen, David M.; Kaess, Michael; Leonard, John J.en_US
dc.identifier.orcidhttps://orcid.org/0000-0002-8863-6550
dc.identifier.orcidhttps://orcid.org/0000-0001-8964-1602
mit.licenseOPEN_ACCESS_POLICYen_US
mit.metadata.statusComplete


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