Concurrent filtering and smoothing
This paper presents a novel algorithm for integrating real-time filtering of navigation data with full map/trajectory smoothing. Unlike conventional mapping strategies, the result of loop closures within the smoother serve to correct the real-time navigation solution in addition to the map. This sol...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers
2013
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| Online Access: | http://hdl.handle.net/1721.1/79077 https://orcid.org/0000-0002-8863-6550 |
| _version_ | 1811082143725518848 |
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| author | Kaess, Michael Williams, Stephen Indelman, Vadim Roberts, Richard Leonard, John Joseph Dellaert, Frank |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Kaess, Michael Williams, Stephen Indelman, Vadim Roberts, Richard Leonard, John Joseph Dellaert, Frank |
| author_sort | Kaess, Michael |
| collection | MIT |
| description | This paper presents a novel algorithm for integrating real-time filtering of navigation data with full map/trajectory smoothing. Unlike conventional mapping strategies, the result of loop closures within the smoother serve to correct the real-time navigation solution in addition to the map. This solution views filtering and smoothing as different operations applied within a single graphical model known as a Bayes tree. By maintaining all information within a single graph, the optimal linear estimate is guaranteed, while still allowing the filter and smoother to operate asynchronously. This approach has been applied to simulated aerial vehicle sensors consisting of a high-speed IMU and stereo camera. Loop closures are extracted from the vision system in an external process and incorporated into the smoother when discovered. The performance of the proposed method is shown to approach that of full batch optimization while maintaining real-time operation. |
| first_indexed | 2024-09-23T11:57:59Z |
| format | Article |
| id | mit-1721.1/79077 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:57:59Z |
| publishDate | 2013 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/790772022-09-27T23:07:57Z Concurrent filtering and smoothing Kaess, Michael Williams, Stephen Indelman, Vadim Roberts, Richard Leonard, John Joseph Dellaert, Frank Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Mechanical Engineering Kaess, Michael Leonard, John Joseph This paper presents a novel algorithm for integrating real-time filtering of navigation data with full map/trajectory smoothing. Unlike conventional mapping strategies, the result of loop closures within the smoother serve to correct the real-time navigation solution in addition to the map. This solution views filtering and smoothing as different operations applied within a single graphical model known as a Bayes tree. By maintaining all information within a single graph, the optimal linear estimate is guaranteed, while still allowing the filter and smoother to operate asynchronously. This approach has been applied to simulated aerial vehicle sensors consisting of a high-speed IMU and stereo camera. Loop closures are extracted from the vision system in an external process and incorporated into the smoother when discovered. The performance of the proposed method is shown to approach that of full batch optimization while maintaining real-time operation. United States. Air Force Research Laboratory (contract FA8650- 11-C-7137) 2013-06-06T20:39:01Z 2013-06-06T20:39:01Z 2012-07 Article http://purl.org/eprint/type/ConferencePaper 978-0-9824438-4-2 978-1-4673-0417-7 INSPEC Accession Number: 12965807 http://hdl.handle.net/1721.1/79077 Kaess, Michael, Stephen Williams, Vadim Indelman, Richard Roberts, John J. Leonard, and Frank Dellaert. "Concurrent Filtering and Smoothing." In 2012 15th International Conference on Information Fusion (FUSION), 9-12 July 2012, Singapore. p.1300-1307 https://orcid.org/0000-0002-8863-6550 en_US http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6289957 Proceedings of the 2012 15th International Conference on Information Fusion (FUSION) Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf Institute of Electrical and Electronics Engineers MIT web domain |
| spellingShingle | Kaess, Michael Williams, Stephen Indelman, Vadim Roberts, Richard Leonard, John Joseph Dellaert, Frank Concurrent filtering and smoothing |
| title | Concurrent filtering and smoothing |
| title_full | Concurrent filtering and smoothing |
| title_fullStr | Concurrent filtering and smoothing |
| title_full_unstemmed | Concurrent filtering and smoothing |
| title_short | Concurrent filtering and smoothing |
| title_sort | concurrent filtering and smoothing |
| url | http://hdl.handle.net/1721.1/79077 https://orcid.org/0000-0002-8863-6550 |
| work_keys_str_mv | AT kaessmichael concurrentfilteringandsmoothing AT williamsstephen concurrentfilteringandsmoothing AT indelmanvadim concurrentfilteringandsmoothing AT robertsrichard concurrentfilteringandsmoothing AT leonardjohnjoseph concurrentfilteringandsmoothing AT dellaertfrank concurrentfilteringandsmoothing |