Consistent cooperative localization
In cooperative navigation, teams of mobile robots obtain range and/or angle measurements to each other and dead-reckoning information to help each other navigate more accurately. One typical approach is moving baseline navigation, in which multiple Autonomous Underwater Vehicles (AUVs) exchange rang...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Institute of Electrical and Electronics Engineers
2010
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/58890 https://orcid.org/0000-0002-8863-6550 |
| _version_ | 1826209963713757184 |
|---|---|
| author | Walter, Matthew R. Bahr, Alexander Leonard, John Joseph |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Walter, Matthew R. Bahr, Alexander Leonard, John Joseph |
| author_sort | Walter, Matthew R. |
| collection | MIT |
| description | In cooperative navigation, teams of mobile robots obtain range and/or angle measurements to each other and dead-reckoning information to help each other navigate more accurately. One typical approach is moving baseline navigation, in which multiple Autonomous Underwater Vehicles (AUVs) exchange range measurements using acoustic modems to perform mobile trilateration. While the sharing of information between vehicles can be highly beneficial, exchanging measurements and state estimates can also be dangerous because of the risk of measurements being used by a vehicle more than once; such data re-use leads to inconsistent (overconfident) estimates, making data association and outlier rejection more difficult and divergence more likely. In this paper, we present a technique for the consistent cooperative localization of multiple AUVs performing mobile trilateration. Each AUV establishes a bank of filters, performing careful bookkeeping to track the origins of measurements and prevent the use any of the measurements more than once. The multiple estimates are combined in a consistent manner, yielding conservative covariance estimates. The technique is illustrated using simulation results. The new method is compared side-by-side with a naive approach that does not keep track of the origins of measurements, illustrating that the new method keeps conservative covariance bounds whereas state estimates obtained with the naive approach become overconfident and diverge. |
| first_indexed | 2024-09-23T14:37:30Z |
| format | Article |
| id | mit-1721.1/58890 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:37:30Z |
| publishDate | 2010 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/588902022-10-01T21:50:43Z Consistent cooperative localization Walter, Matthew R. Bahr, Alexander Leonard, John Joseph Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Walter, Matthew R. Walter, Matthew R. Bahr, Alexander Leonard, John Joseph Sensor Fusion Cooperative Localization Cooperative Navigation In cooperative navigation, teams of mobile robots obtain range and/or angle measurements to each other and dead-reckoning information to help each other navigate more accurately. One typical approach is moving baseline navigation, in which multiple Autonomous Underwater Vehicles (AUVs) exchange range measurements using acoustic modems to perform mobile trilateration. While the sharing of information between vehicles can be highly beneficial, exchanging measurements and state estimates can also be dangerous because of the risk of measurements being used by a vehicle more than once; such data re-use leads to inconsistent (overconfident) estimates, making data association and outlier rejection more difficult and divergence more likely. In this paper, we present a technique for the consistent cooperative localization of multiple AUVs performing mobile trilateration. Each AUV establishes a bank of filters, performing careful bookkeeping to track the origins of measurements and prevent the use any of the measurements more than once. The multiple estimates are combined in a consistent manner, yielding conservative covariance estimates. The technique is illustrated using simulation results. The new method is compared side-by-side with a naive approach that does not keep track of the origins of measurements, illustrating that the new method keeps conservative covariance bounds whereas state estimates obtained with the naive approach become overconfident and diverge. 2010-10-06T14:29:51Z 2010-10-06T14:29:51Z 2009-07 2009-05 Article http://purl.org/eprint/type/JournalArticle 978-1-4244-2788-8 1050-4729 INSPEC Accession Number: 10749164 http://hdl.handle.net/1721.1/58890 Bahr, A., M.R. Walter, and J.J. Leonard. “Consistent cooperative localization.” Robotics and Automation, 2009. ICRA '09. IEEE International Conference on. 2009. 3415-3422. ©2009 Institute of Electrical and Electronics Engineers. https://orcid.org/0000-0002-8863-6550 en_US http://dx.doi.org/10.1109/ROBOT.2009.5152859 IEEE International Conference on Robotics and Automation, 2009. ICRA '09 Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers IEEE |
| spellingShingle | Sensor Fusion Cooperative Localization Cooperative Navigation Walter, Matthew R. Bahr, Alexander Leonard, John Joseph Consistent cooperative localization |
| title | Consistent cooperative localization |
| title_full | Consistent cooperative localization |
| title_fullStr | Consistent cooperative localization |
| title_full_unstemmed | Consistent cooperative localization |
| title_short | Consistent cooperative localization |
| title_sort | consistent cooperative localization |
| topic | Sensor Fusion Cooperative Localization Cooperative Navigation |
| url | http://hdl.handle.net/1721.1/58890 https://orcid.org/0000-0002-8863-6550 |
| work_keys_str_mv | AT waltermatthewr consistentcooperativelocalization AT bahralexander consistentcooperativelocalization AT leonardjohnjoseph consistentcooperativelocalization |