Consistent cooperative localization
Name
Bahr-2009-Consistent cooperative localization.pdf
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490.13 KB
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Adobe PDF
Checksum (MD5)
a6500adebbc34ec1428c1bf7b5606fb4
Author(s)
Walter, Matthew R.
Bahr, Alexander
Leonard, John Joseph
Date Issued
July 2009
Journal
IEEE International Conference on Robotics and Automation, 2009. ICRA '09
Publisher
Institute of Electrical and Electronics Engineers
Citation
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.
Version
Final published version
Abstract
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.
Subjects
Sensor Fusion
Cooperative Localization
Cooperative Navigation
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
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.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ROBOT.2009.5152859
