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Factor graph optimization for underwater vehicle navigation

Paper Authors:

Jiangbo Song,

Wanqing Li,

Ruofan Liu,

Xiangwei Zhu


Key Details

Proposes factor graph optimization framework for underwater vehicle navigation

Achieves tight integration of inertial and acoustic sensors

Handles asynchronous and heterogeneous sensor data

More robust and accurate than Kalman filter methods

Validated through simulations and real-world experiments

AI generated summary

Factor graph optimization for underwater vehicle navigation

This paper proposes a factor graph optimization approach for fusing data from multiple sensors to enable accurate navigation and positioning for autonomous underwater vehicles. It tightly couples inertial sensors with acoustic positioning to provide robust and precise state estimation.

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