DVL Calibration using Data-driven Methods
Zeev Yampolsky Itzik Klein
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.