BIND-USBL: Bounding IMU Navigation Drift using USBL in Heterogeneous ASV-AUV Teams
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866914470018678784 |
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| author | Kedia, Pranav Makam, Rajini Hamann, Heiko Sundaram, Suresh |
| author_facet | Kedia, Pranav Makam, Rajini Hamann, Heiko Sundaram, Suresh |
| contents | Accurate and continuous localization of Autonomous Underwater Vehicles (AUVs) in GPS-denied environments is a persistent challenge in marine robotics. In the absence of external position fixes, AUVs rely on inertial dead-reckoning, which accumulates unbounded drift due to sensor bias and noise. This paper presents BIND-USBL, a cooperative localization framework in which a fleet of Autonomous Surface Vessels (ASVs) equipped with Ultra-Short Baseline (USBL) acoustic positioning systems provides intermittent fixes to bound AUV dead-reckoning error. The key insight is that long-duration navigation failure is driven not by the accuracy of individual USBL measurements, but by the temporal sparsity and geometric availability of those fixes. BIND-USBL combines a multi-ASV formation model linking survey scale and anchor placement to acoustic coverage, a conflict-graph-based TDMA uplink scheduler for shared-channel servicing, and delayed fusion of received USBL updates with drift-prone dead reckoning. The framework is evaluated in the HoloOcean simulator using heterogeneous ASV-AUV teams executing lawnmower coverage missions. The results show that localization performance is shaped by the interaction of survey scale, acoustic coverage, team composition, and ASV-formation geometry. Further, the spatial-reuse scheduler improves per-AUV fix delivery rate without violating the no-collision constraint, while maintaining low end-to-end fix latency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_11861 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | BIND-USBL: Bounding IMU Navigation Drift using USBL in Heterogeneous ASV-AUV Teams Kedia, Pranav Makam, Rajini Hamann, Heiko Sundaram, Suresh Robotics Multiagent Systems Accurate and continuous localization of Autonomous Underwater Vehicles (AUVs) in GPS-denied environments is a persistent challenge in marine robotics. In the absence of external position fixes, AUVs rely on inertial dead-reckoning, which accumulates unbounded drift due to sensor bias and noise. This paper presents BIND-USBL, a cooperative localization framework in which a fleet of Autonomous Surface Vessels (ASVs) equipped with Ultra-Short Baseline (USBL) acoustic positioning systems provides intermittent fixes to bound AUV dead-reckoning error. The key insight is that long-duration navigation failure is driven not by the accuracy of individual USBL measurements, but by the temporal sparsity and geometric availability of those fixes. BIND-USBL combines a multi-ASV formation model linking survey scale and anchor placement to acoustic coverage, a conflict-graph-based TDMA uplink scheduler for shared-channel servicing, and delayed fusion of received USBL updates with drift-prone dead reckoning. The framework is evaluated in the HoloOcean simulator using heterogeneous ASV-AUV teams executing lawnmower coverage missions. The results show that localization performance is shaped by the interaction of survey scale, acoustic coverage, team composition, and ASV-formation geometry. Further, the spatial-reuse scheduler improves per-AUV fix delivery rate without violating the no-collision constraint, while maintaining low end-to-end fix latency. |
| title | BIND-USBL: Bounding IMU Navigation Drift using USBL in Heterogeneous ASV-AUV Teams |
| topic | Robotics Multiagent Systems |
| url | https://arxiv.org/abs/2604.11861 |