Stable Multi-Drone GNSS Tracking System for Marine Robots
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917323912249344 |
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| author | Wen, Shuo Meriaux, Edwin Guzmán, Mariana Sosa Wang, Zhizun Shi, Junming Dudek, Gregory |
| author_facet | Wen, Shuo Meriaux, Edwin Guzmán, Mariana Sosa Wang, Zhizun Shi, Junming Dudek, Gregory |
| contents | Stable and accurate tracking is essential for marine robotics, yet Global Navigation Satellite System (GNSS) signals vanish immediately below the sea surface. Traditional alternatives suffer from error accumulation, high computational demands, or infrastructure dependence. In this work, we present a multi-drone GNSS-based tracking system for surface and near-surface marine robots. Our approach combines efficient visual detection, lightweight multi-object tracking, GNSS-based triangulation, and a confidence-weighted Extended Kalman Filter (EKF) to provide stable GNSS estimation in real time. We further introduce a cross-drone tracking ID alignment algorithm that enforces global consistency across views, enabling robust multi-robot tracking with cooperative aerial coverage. We validate our system in diversified complex settings to show the accuracy and robustness of the proposed algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18694 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stable Multi-Drone GNSS Tracking System for Marine Robots Wen, Shuo Meriaux, Edwin Guzmán, Mariana Sosa Wang, Zhizun Shi, Junming Dudek, Gregory Robotics Artificial Intelligence Computer Vision and Pattern Recognition Stable and accurate tracking is essential for marine robotics, yet Global Navigation Satellite System (GNSS) signals vanish immediately below the sea surface. Traditional alternatives suffer from error accumulation, high computational demands, or infrastructure dependence. In this work, we present a multi-drone GNSS-based tracking system for surface and near-surface marine robots. Our approach combines efficient visual detection, lightweight multi-object tracking, GNSS-based triangulation, and a confidence-weighted Extended Kalman Filter (EKF) to provide stable GNSS estimation in real time. We further introduce a cross-drone tracking ID alignment algorithm that enforces global consistency across views, enabling robust multi-robot tracking with cooperative aerial coverage. We validate our system in diversified complex settings to show the accuracy and robustness of the proposed algorithm. |
| title | Stable Multi-Drone GNSS Tracking System for Marine Robots |
| topic | Robotics Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.18694 |