iSAM: Incremental Smoothing and Mapping
In this paper, we present incremental smoothing and mapping (iSAM), which is a novel approach to the simultaneous localization and mapping problem that is based on fast incremental matrix factorization. iSAM provides an efficient and exact solution by updating a QR factorization of the naturally spa...
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| Veröffentlicht in: | IEEE transactions on robotics 2008-12, Vol.24 (6), p.1365-1378 |
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| container_title | IEEE transactions on robotics |
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| creator | Kaess, M. Ranganathan, A. Dellaert, F. |
| description | In this paper, we present incremental smoothing and mapping (iSAM), which is a novel approach to the simultaneous localization and mapping problem that is based on fast incremental matrix factorization. iSAM provides an efficient and exact solution by updating a QR factorization of the naturally sparse smoothing information matrix, thereby recalculating only those matrix entries that actually change. iSAM is efficient even for robot trajectories with many loops as it avoids unnecessary fill-in in the factor matrix by periodic variable reordering. Also, to enable data association in real time, we provide efficient algorithms to access the estimation uncertainties of interest based on the factored information matrix. We systematically evaluate the different components of iSAM as well as the overall algorithm using various simulated and real-world datasets for both landmark and pose-only settings. |
| doi_str_mv | 10.1109/TRO.2008.2006706 |
| format | Article |
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Also, to enable data association in real time, we provide efficient algorithms to access the estimation uncertainties of interest based on the factored information matrix. We systematically evaluate the different components of iSAM as well as the overall algorithm using various simulated and real-world datasets for both landmark and pose-only settings.</description><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Computer science; control theory; systems</subject><subject>Computer simulation</subject><subject>Control theory. Systems</subject><subject>Data association</subject><subject>Data processing. List processing. Character string processing</subject><subject>Data smoothing</subject><subject>Exact sciences and technology</subject><subject>Exact solutions</subject><subject>Factorization</subject><subject>Information filtering</subject><subject>Large-scale systems</subject><subject>localization</subject><subject>Mapping</subject><subject>Mathematical analysis</subject><subject>Memory organisation. Data processing</subject><subject>Mobile robots</subject><subject>nonlinear estimation</subject><subject>Robot sensing systems</subject><subject>Robotics</subject><subject>Robots</subject><subject>Simultaneous localization and mapping</subject><subject>simultaneous localization and mapping (SLAM)</subject><subject>Smoothing</subject><subject>Smoothing methods</subject><subject>Software</subject><subject>Sparse matrices</subject><subject>Trajectory</subject><subject>Uncertainty</subject><issn>1552-3098</issn><issn>1941-0468</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp9kctLAzEQhxdRsFbvgpdF0HrZOnkn3krxUWgp2HoO2WxWV_ZRN9uD_71ZWnrw0EsyMN_8mOGLomsEY4RAPa7fl2MMIPuHC-An0QApihKgXJ6GmjGcEFDyPLrw_hsAUwVkEI2K1WTxFM9q27rK1Z0p41XVNN1XUX_Gps7ihdlsQn0ZneWm9O5q_w-jj5fn9fQtmS9fZ9PJPLEMWJdwZTChgFNukWQqNSjLBVLCUCKJtUpKQYUyLktTxcIGLLXM5VggSLNUyYwMo9Eud9M2P1vnO10V3rqyNLVrtl6HpTmRguFA3h8lCaWMUywD-HAURFwgTLlQKqC3_9DvZtvW4WCNAXEIl9AAwQ6ybeN963K9aYvKtL8age5d6OBC9y703kUYudvnGm9NmbemtoU_zGEUfEjcR9_suMI5d2gHg1gQRP4ACsaOAA</recordid><startdate>20081201</startdate><enddate>20081201</enddate><creator>Kaess, M.</creator><creator>Ranganathan, A.</creator><creator>Dellaert, F.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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List processing. Character string processing</topic><topic>Data smoothing</topic><topic>Exact sciences and technology</topic><topic>Exact solutions</topic><topic>Factorization</topic><topic>Information filtering</topic><topic>Large-scale systems</topic><topic>localization</topic><topic>Mapping</topic><topic>Mathematical analysis</topic><topic>Memory organisation. Data processing</topic><topic>Mobile robots</topic><topic>nonlinear estimation</topic><topic>Robot sensing systems</topic><topic>Robotics</topic><topic>Robots</topic><topic>Simultaneous localization and mapping</topic><topic>simultaneous localization and mapping (SLAM)</topic><topic>Smoothing</topic><topic>Smoothing methods</topic><topic>Software</topic><topic>Sparse matrices</topic><topic>Trajectory</topic><topic>Uncertainty</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kaess, M.</creatorcontrib><creatorcontrib>Ranganathan, A.</creatorcontrib><creatorcontrib>Dellaert, F.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><jtitle>IEEE transactions on robotics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Kaess, M.</au><au>Ranganathan, A.</au><au>Dellaert, F.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>iSAM: Incremental Smoothing and Mapping</atitle><jtitle>IEEE transactions on robotics</jtitle><stitle>TRO</stitle><date>2008-12-01</date><risdate>2008</risdate><volume>24</volume><issue>6</issue><spage>1365</spage><epage>1378</epage><pages>1365-1378</pages><issn>1552-3098</issn><eissn>1941-0468</eissn><coden>ITREAE</coden><abstract>In this paper, we present incremental smoothing and mapping (iSAM), which is a novel approach to the simultaneous localization and mapping problem that is based on fast incremental matrix factorization. iSAM provides an efficient and exact solution by updating a QR factorization of the naturally sparse smoothing information matrix, thereby recalculating only those matrix entries that actually change. iSAM is efficient even for robot trajectories with many loops as it avoids unnecessary fill-in in the factor matrix by periodic variable reordering. 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| subjects | Algorithms Applied sciences Computer science control theory systems Computer simulation Control theory. Systems Data association Data processing. List processing. Character string processing Data smoothing Exact sciences and technology Exact solutions Factorization Information filtering Large-scale systems localization Mapping Mathematical analysis Memory organisation. Data processing Mobile robots nonlinear estimation Robot sensing systems Robotics Robots Simultaneous localization and mapping simultaneous localization and mapping (SLAM) Smoothing Smoothing methods Software Sparse matrices Trajectory Uncertainty |
| title | iSAM: Incremental Smoothing and Mapping |
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