Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
A Probabilistic Approach to Collaborative Multi-Robot Localization
Autonomous Robots
Distributed suboptimal cooperative localization for multiple underwater vehicles
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
A collaborative localization tolerant to recognition error by double-check particle exchange
Artificial Life and Robotics
A decentralized junction tree approach to mobile robots cooperative localization
ICIRA'10 Proceedings of the Third international conference on Intelligent robotics and applications - Volume Part I
Large scale multiple robot visual mapping with heterogeneous landmarks in semi-structured terrain
Robotics and Autonomous Systems
Distributed vision-aided cooperative localization and navigation based on three-view geometry
Robotics and Autonomous Systems
Graph-based distributed cooperative navigation for a general multi-robot measurement model
International Journal of Robotics Research
A “thermodynamic” approach to multi-robot cooperative localization
Theoretical Computer Science
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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.