TY - GEN
T1 - Localization of an Unmanned Underwater Vehicle Using a Tethered Cooperative Surface Vehicle and Hybrid EKF/Grid-Based Method
AU - Oxford, A. Malori
AU - Vu, Nathan
AU - Furukawa, Tomonari
AU - Englot, Brendan
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - This paper presents an approach for the localization of an Unmanned Underwater Vehicle (UUV) in a cooperative team with a tethered Unmanned Surface Vehicle (USV). For the localization, the UUV and the USV carry a camera and a sonar respectively to observe each other. The vehicle states are split between Extended Kalman Filter and grid-based estimators based on which sensors provide Gaussian or non-Gaussian observations of each state. Specifically, the horizontal position of the UUV is estimated using a grid-based method because the camera and sonar that observe these states provide non-Gaussian observations when they cannot detect their target. Additionally, the tether to the USV is treated as a non-Gaussian observation that prevents unbounded error growth. Validation of the technique was performed in simulations using sensor models developed based on testing in a lake and pool.
AB - This paper presents an approach for the localization of an Unmanned Underwater Vehicle (UUV) in a cooperative team with a tethered Unmanned Surface Vehicle (USV). For the localization, the UUV and the USV carry a camera and a sonar respectively to observe each other. The vehicle states are split between Extended Kalman Filter and grid-based estimators based on which sensors provide Gaussian or non-Gaussian observations of each state. Specifically, the horizontal position of the UUV is estimated using a grid-based method because the camera and sonar that observe these states provide non-Gaussian observations when they cannot detect their target. Additionally, the tether to the USV is treated as a non-Gaussian observation that prevents unbounded error growth. Validation of the technique was performed in simulations using sensor models developed based on testing in a lake and pool.
UR - https://www.scopus.com/pages/publications/105029984021
UR - https://www.scopus.com/pages/publications/105029984021#tab=citedBy
U2 - 10.1109/IROS60139.2025.11246446
DO - 10.1109/IROS60139.2025.11246446
M3 - Conference contribution
AN - SCOPUS:105029984021
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 7051
EP - 7056
BT - IROS 2025 - 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems, Conference Proceedings
A2 - Laugier, Christian
A2 - Renzaglia, Alessandro
A2 - Atanasov, Nikolay
A2 - Birchfield, Stan
A2 - Cielniak, Grzegorz
A2 - De Mattos, Leonardo
A2 - Fiorini, Laura
A2 - Giguere, Philippe
A2 - Hashimoto, Kenji
A2 - Ibanez-Guzman, Javier
A2 - Kamegawa, Tetsushi
A2 - Lee, Jinoh
A2 - Loianno, Giuseppe
A2 - Luck, Kevin
A2 - Maruyama, Hisataka
A2 - Martinet, Philippe
A2 - Moradi, Hadi
A2 - Nunes, Urbano
A2 - Pettre, Julien
A2 - Pretto, Alberto
A2 - Ranzani, Tommaso
A2 - Ronnau, Arne
A2 - Rossi, Silvia
A2 - Rouse, Elliott
A2 - Ruggiero, Fabio
A2 - Simonin, Olivier
A2 - Wang, Danwei
A2 - Yang, Ming
A2 - Yoshida, Eiichi
A2 - Zhao, Huijing
T2 - 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2025
Y2 - 19 October 2025 through 25 October 2025
ER -