An Experimental Study of a Cooperative Positioning System
Autonomous Robots
Introduction to Autonomous Mobile Robots
Introduction to Autonomous Mobile Robots
Distributed Cooperative Outdoor Multirobot Localization and Mapping
Autonomous Robots
Multi-robot Simultaneous Localization and Mapping using Particle Filters
International Journal of Robotics Research
Decentralized localization for dynamic and sparse robot networks
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Distributed maximum a posteriori estimation for multi-robot cooperative localization
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Cooperative multi-robot localization under communication constraints
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Consensus in Ad Hoc WSNs With Noisy Links—Part I: Distributed Estimation of Deterministic Signals
IEEE Transactions on Signal Processing
Coordinated multi-robot exploration
IEEE Transactions on Robotics
The UTIAS multi-robot cooperative localization and mapping dataset
International Journal of Robotics Research
Journal of Intelligent and Robotic Systems
Robotic clusters: Multi-robot systems as computer clusters
Robotics and Autonomous Systems
A “thermodynamic” approach to multi-robot cooperative localization
Theoretical Computer Science
Mutual localization in multi-robot systems using anonymous relative measurements
International Journal of Robotics Research
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Finite-range sensing and communication are factors in the connectivity of a dynamic mobile-robot network. State estimation becomes a difficult problem when communication connections allowing information exchange between all robots are not guaranteed. This paper presents a decentralized state-estimation algorithm guaranteed to work in dynamic robot networks without connectivity requirements. We prove that a robot only needs to consider its own knowledge of network topology in order to produce an estimate equivalent to the centralized state estimate whenever possible while ensuring that the same can be performed by all other robots in the network. We prove certain properties of our technique and then it is validated through simulations. We present a comprehensive set of results, indicating the performance benefit in different network connectivity settings, as well as the scalability of our approach.