dc.contributor.author | Kaess, Michael | |
dc.contributor.author | Rosen, David Matthew | |
dc.contributor.author | Leonard, John Joseph | |
dc.date.accessioned | 2015-06-30T14:28:14Z | |
dc.date.available | 2015-06-30T14:28:14Z | |
dc.date.issued | 2014-09 | |
dc.date.submitted | 2014-01 | |
dc.identifier.issn | 1552-3098 | |
dc.identifier.issn | 1941-0468 | |
dc.identifier.uri | http://hdl.handle.net/1721.1/97576 | |
dc.description.abstract | Many 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.sponsorship | United States. Office of Naval Research (Grant N00014-12-1-0093) | en_US |
dc.description.sponsorship | United States. Office of Naval Research (Grant N00014-11-1-0688) | en_US |
dc.description.sponsorship | United States. Office of Naval Research (Grant N00014-06-1-0043) | en_US |
dc.description.sponsorship | United States. Office of Naval Research (Grant N00014-10-1-0936) | en_US |
dc.description.sponsorship | United States. Air Force Research Laboratory (Contract FA8650-11-C-7137) | 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/tro.2014.2321852 | 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 | Other univ. web domain | en_US |
dc.title | RISE: An Incremental Trust-Region Method for Robust Online Sparse Least-Squares Estimation | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Rosen, 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.department | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Mechanical Engineering | en_US |
dc.contributor.mitauthor | Rosen, David Matthew | en_US |
dc.contributor.mitauthor | Leonard, John Joseph | en_US |
dc.relation.journal | IEEE Transactions on Robotics | en_US |
dc.eprint.version | Author's final manuscript | en_US |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
dspace.orderedauthors | Rosen, David M.; Kaess, Michael; Leonard, John J. | en_US |
dc.identifier.orcid | https://orcid.org/0000-0002-8863-6550 | |
dc.identifier.orcid | https://orcid.org/0000-0001-8964-1602 | |
mit.license | OPEN_ACCESS_POLICY | en_US |
mit.metadata.status | Complete | |