DVL Calibration using Data-driven Methods
This work addresses calibration efficiency for AUV navigation, but it appears incremental as it applies deep learning to an existing calibration task.
The paper tackles the problem of calibrating Doppler velocity logs (DVLs) for autonomous underwater vehicles by proposing an end-to-end deep-learning framework, which outperforms model-based approaches by 35% in accuracy and 80% in calibration time using stimulative data.
Autonomous underwater vehicles (AUVs) are used in a wide range of underwater applications, ranging from seafloor mapping to industrial operations. While underwater, the AUV navigation solution commonly relies on the fusion between inertial sensors and Doppler velocity logs (DVL). To achieve accurate DVL measurements a calibration procedure should be conducted before the mission begins. Model-based calibration approaches include filtering approaches utilizing global navigation satellite system signals. In this paper, we propose an end-to-end deep-learning framework for the calibration procedure. Using stimulative data, we show that our proposed approach outperforms model-based approaches by 35% in accuracy and 80% in the required calibration time.