LiBeamsNet: AUV Velocity Vector Estimation in Situations of Limited DVL Beam Measurements

TL;DR

LiBeamsNet is a deep learning framework that estimates missing DVL beam velocities using inertial data, enabling accurate AUV velocity vector estimation even with incomplete beam measurements in complex underwater environments.

Contribution

The paper introduces LiBeamsNet, a novel deep learning method that regresses missing DVL beams from inertial data, improving AUV navigation when beam measurements are incomplete.

Findings

Achieved up to 7.2% speed error in velocity estimation.

Validated with sea experiments in the Mediterranean Sea.

Effective in scenarios with less than three beam measurements.

Abstract

Autonomous underwater vehicles (AUVs) are employed for marine applications and can operate in deep underwater environments beyond human reach. A standard solution for the autonomous navigation problem can be obtained by fusing the inertial navigation system and the Doppler velocity log sensor (DVL). The latter measures four beam velocities to estimate the vehicle's velocity vector. In real-world scenarios, the DVL may receive less than three beam velocities if the AUV operates in complex underwater environments. In such conditions, the vehicle's velocity vector could not be estimated leading to a navigation solution drift and in some situations the AUV is required to abort the mission and return to the surface. To circumvent such a situation, in this paper we propose a deep learning framework, LiBeamsNet, that utilizes the inertial data and the partial beam velocities to regress the missing beams in two missing beams scenarios. Once all the beams are obtained, the vehicle's velocity vector can be estimated. The approach performance was validated by sea experiments in the Mediterranean Sea. The results show up to 7.2% speed error in the vehicle's velocity vector estimation in a scenario that otherwise could not provide an estimate.