Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
The Cricket location-support system
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Nonparametric belief propagation for self-calibration in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Tracking moving devices with the cricket location system
Proceedings of the 2nd international conference on Mobile systems, applications, and services
A distributed algorithm for constructing a minimum diameter spanning tree
Journal of Parallel and Distributed Computing
Robust distributed network localization with noisy range measurements
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Distributed localization of networked cameras
Proceedings of the 5th international conference on Information processing in sensor networks
Reaching a Consensus in a Dynamically Changing Environment: A Graphical Approach
SIAM Journal on Control and Optimization
Modeling mobile robot motion with polar representations
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Synchronous-clock, one-way-travel-time acoustic navigation for underwater vehicles
Journal of Field Robotics
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Decentralized sensor fusion with distributed particle filters
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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A key problem in the deployment of sensor networks is that of determining the location of each sensor such that subsequent data gathered can be registered. We would also like the network to provide localization for mobile entities, allowing them to navigate and explore the environment. In this paper, we present a thorough evaluation of our algorithm for localizing and mapping the mobile and stationary nodes in sparsely connected sensor networks using range-only measurements and odometry from the mobile node. Our approach utilizes an extended Kalman filter (EKF) in polar space allowing us to model the non-linearity within the range-only measurements using Gaussian distributions. Utilizing the motion information from a mobile node, we show additional improvements to the static network localization solution. In addition to this centralized filtering technique, an asynchronous and decentralized approach is investigated and experimentally proven. This decentralized filtering technique distributes the computation across all nodes in the network, leveraging their numbers for improved efficiency. We demonstrate the effectiveness of our approach using simulated and real-world experiments in challenging environments with limited network connectivity. Our results reveal that our proposed method offers good accuracy in these challenging environments even when little to no prior information is available. Additionally, it is shown that by initializing the network map with a static network solution, the network mapping with a mobile node can be further improved.