ResAlignNet: A Data-Driven Approach for INS/DVL Alignment

TL;DR

ResAlignNet is a data-driven deep learning method that rapidly aligns INS and DVL sensors in underwater vehicles without external aids, achieving high accuracy in seconds and enabling flexible, immediate deployment.

Contribution

It introduces ResAlignNet, a novel deep neural network approach that significantly reduces alignment time and removes motion pattern dependencies in underwater navigation systems.

Findings

Achieves alignment accuracy within 0.8° using 25 seconds of data.

Reduces convergence time by 65% compared to standard methods.

Operates without external aids or complex maneuvers, enabling immediate deployment.

Abstract

Autonomous underwater vehicles rely on precise navigation systems that combine the inertial navigation system and the Doppler velocity log for successful missions in challenging environments where satellite navigation is unavailable. The effectiveness of this integration critically depends on accurate alignment between the sensor reference frames. Standard model-based alignment methods between these sensor systems suffer from lengthy convergence times, dependence on prescribed motion patterns, and reliance on external aiding sensors, significantly limiting operational flexibility. To address these limitations, this paper presents ResAlignNet, a data-driven approach using the 1D ResNet-18 architecture that transforms the alignment problem into deep neural network optimization, operating as an in-situ solution that requires only sensors on board without external positioning aids or complex vehicle maneuvers, while achieving rapid convergence in seconds. Additionally, the approach demonstrates the learning capabilities of Sim2Real transfer, enabling training in synthetic data while deploying in operational sensor measurements. Experimental validation using the Snapir autonomous underwater vehicle demonstrates that ResAlignNet achieves alignment accuracy within 0.8{\deg} using only 25 seconds of data collection, representing a 65\% reduction in convergence time compared to standard velocity-based methods. The trajectory-independent solution eliminates motion pattern requirements and enables immediate vehicle deployment without lengthy pre-mission procedures, advancing underwater navigation capabilities through robust sensor-agnostic alignment that scales across different operational scenarios and sensor specifications.