An Experimental Study of a Cooperative Positioning System
Autonomous Robots
Multi-robot Simultaneous Localization and Mapping using Particle Filters
International Journal of Robotics Research
Delayed-state information filter for cooperative decentralized tracking
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
IEEE Transactions on Robotics
The UTIAS multi-robot cooperative localization and mapping dataset
International Journal of Robotics Research
Decentralized sensor fusion with distributed particle filters
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Distributed robotic sensor networks: An information-theoretic approach
International Journal of Robotics Research
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Communication between robots is key to performance in cooperative multi-robot systems. In practice, communication connections for information exchange between all robots are not always guaranteed, which adds difficulty in performing state estimation. This paper examines the decentralized cooperative simultaneous localization and mapping (SLAM) problem, in which each robot is required to estimate the map and all robot states under a sparsely-communicating and dynamic network. We show how the exact, centralized-equivalent estimate can be obtained by all robots in the network in a decentralized manner even when the network is never fully connected. Furthermore, a robot only needs to consider its own knowledge of the network topology in order to detect when the centralized-equivalent estimate is obtainable. Our approach is validated through more than 250 min of hardware experiments using a team of real robots. The resulting estimates are compared against accurate groundtruth data for all robot poses and landmark positions. In addition, we examined the effects of communication range limit on our algorithm's performance.