Extending the Limits of Feature-Based SLAM With B-Splines
This paper describes a simultaneous localization and mapping (SLAM) algorithm for use in unstructured environments that is effective regardless of the geometric complexity of the environment. Features are described using B-splines as modeling tool, and the set of control points defining their shape...
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| Veröffentlicht in: | IEEE transactions on robotics 2009-04, Vol.25 (2), p.353-366 |
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| container_end_page | 366 |
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| container_start_page | 353 |
| container_title | IEEE transactions on robotics |
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| creator | Pedraza, L. Rodriguez-Losada, D. Matia, F. Dissanayake, G. Miro, J.V. |
| description | This paper describes a simultaneous localization and mapping (SLAM) algorithm for use in unstructured environments that is effective regardless of the geometric complexity of the environment. Features are described using B-splines as modeling tool, and the set of control points defining their shape is used to form a complete and compact description of the environment, thus making it feasible to use an extended Kalman-filter (EKF) based SLAM algorithm. This method is the first known EKF-SLAM implementation capable of describing general free-form features in a parametric manner. Efficient strategies for computing the relevant Jacobians, perform data association, initialization, and map enlargement are presented. The algorithms are evaluated for accuracy and consistency using computer simulations, and for effectiveness using experimental data gathered from different real environments. |
| doi_str_mv | 10.1109/TRO.2009.2013496 |
| format | Article |
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Features are described using B-splines as modeling tool, and the set of control points defining their shape is used to form a complete and compact description of the environment, thus making it feasible to use an extended Kalman-filter (EKF) based SLAM algorithm. This method is the first known EKF-SLAM implementation capable of describing general free-form features in a parametric manner. Efficient strategies for computing the relevant Jacobians, perform data association, initialization, and map enlargement are presented. 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Features are described using B-splines as modeling tool, and the set of control points defining their shape is used to form a complete and compact description of the environment, thus making it feasible to use an extended Kalman-filter (EKF) based SLAM algorithm. This method is the first known EKF-SLAM implementation capable of describing general free-form features in a parametric manner. Efficient strategies for computing the relevant Jacobians, perform data association, initialization, and map enlargement are presented. The algorithms are evaluated for accuracy and consistency using computer simulations, and for effectiveness using experimental data gathered from different real environments.</description><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Computer science; control theory; systems</subject><subject>Computer simulation</subject><subject>Consistency</subject><subject>Control theory. 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Systems</topic><topic>Enlargement</topic><topic>Exact sciences and technology</topic><topic>Filtering</topic><topic>Intelligent control</topic><topic>Jacobian matrices</topic><topic>Jacobians</topic><topic>Kalman filtering</topic><topic>Kalman filters</topic><topic>Laser modes</topic><topic>Mapping</topic><topic>Mathematical models</topic><topic>Mobile robots</topic><topic>Robot sensing systems</topic><topic>Robotics</topic><topic>Shape control</topic><topic>Simulation</topic><topic>Simultaneous localization and mapping</topic><topic>simultaneous localization and mapping (SLAM)</topic><topic>Spline</topic><topic>spline functions</topic><topic>Strategy</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pedraza, L.</creatorcontrib><creatorcontrib>Rodriguez-Losada, D.</creatorcontrib><creatorcontrib>Matia, F.</creatorcontrib><creatorcontrib>Dissanayake, G.</creatorcontrib><creatorcontrib>Miro, J.V.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE 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>Pedraza, L.</au><au>Rodriguez-Losada, D.</au><au>Matia, F.</au><au>Dissanayake, G.</au><au>Miro, J.V.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Extending the Limits of Feature-Based SLAM With B-Splines</atitle><jtitle>IEEE transactions on robotics</jtitle><stitle>TRO</stitle><date>2009-04-01</date><risdate>2009</risdate><volume>25</volume><issue>2</issue><spage>353</spage><epage>366</epage><pages>353-366</pages><issn>1552-3098</issn><eissn>1941-0468</eissn><coden>ITREAE</coden><abstract>This paper describes a simultaneous localization and mapping (SLAM) algorithm for use in unstructured environments that is effective regardless of the geometric complexity of the environment. 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| ispartof | IEEE transactions on robotics, 2009-04, Vol.25 (2), p.353-366 |
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| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Applied sciences Computer science control theory systems Computer simulation Consistency Control theory. Systems Enlargement Exact sciences and technology Filtering Intelligent control Jacobian matrices Jacobians Kalman filtering Kalman filters Laser modes Mapping Mathematical models Mobile robots Robot sensing systems Robotics Shape control Simulation Simultaneous localization and mapping simultaneous localization and mapping (SLAM) Spline spline functions Strategy |
| title | Extending the Limits of Feature-Based SLAM With B-Splines |
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