A Probabilistic Approach to Collaborative Multi-Robot Localization
Autonomous Robots
Distributed Cooperative Outdoor Multirobot Localization and Mapping
Autonomous Robots
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Localization in underwater sensor networks: survey and challenges
WUWNet '06 Proceedings of the 1st ACM international workshop on Underwater networks
Sensor networks of freely drifting autonomous underwater explorers
WUWNet '06 Proceedings of the 1st ACM international workshop on Underwater networks
Localization with Dive'N'Rise (DNR) beacons for underwater acoustic sensor networks
Proceedings of the second workshop on Underwater networks
LOCALE: Collaborative Localization Estimation for Sparse Mobile Sensor Networks
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
Motion-aware self-localization for underwater networks
Proceedings of the third ACM international workshop on Underwater Networks
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Energy-Efficient Ranging for Post-Facto Self-Localization in Mobile Underwater Networks
IEEE Journal on Selected Areas in Communications
Real-time collaborative tracking for underwater networked systems
Proceedings of the Seventh ACM International Conference on Underwater Networks and Systems
Minimizing position uncertainty for under-ice autonomous underwater vehicles
Computer Networks: The International Journal of Computer and Telecommunications Networking
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A key requirement of underwater sensing systems is to track the position of devices while submerged. Traditionally, such tracking has relied on inertial navigation units and acoustic range measurements with surface beacons to estimate node positions over time. While effective for small clusters of instruments, these approaches do not scale well when underwater systems become truly networked. Instead, networked instruments can rely on collaborative localization techniques. However, as these only estimate node positions for time snapshots of the network, this solution breaks down in sparse deployments. In this paper we propose an innovative new approach, collaborative tracking, that marries the benefits from both existing solutions, while overcoming their disadvantages. Our scheme provides complete 4D trajectory estimation, leveraging both time and spatial dimensions, and operates well into regions where both surface beacons and network connectivity are sparse.