Deep Learning Powered Estimate of The Extrinsic Parameters on Unmanned Surface Vehicles

TL;DR

This paper presents a deep learning approach using a Time-Sequence GRNN to improve real-time sensor calibration on USVs, addressing challenges posed by dynamic marine environments and outperforming traditional methods.

Contribution

Introduces a novel deep learning architecture that predicts and refines USV sensor parameters in real time using simulation-trained neural networks.

Findings

Time-Sequence GRNN achieves lowest MSE loss.

Method outperforms traditional neural networks.

Enhances sensor calibration accuracy in marine conditions.

Abstract

Unmanned Surface Vehicles (USVs) are pivotal in marine exploration, but their sensors' accuracy is compromised by the dynamic marine environment. Traditional calibration methods fall short in these conditions. This paper introduces a deep learning architecture that predicts changes in the USV's dynamic metacenter and refines sensors' extrinsic parameters in real time using a Time-Sequence General Regression Neural Network (GRNN) with Euler angles as input. Simulation data from Unity3D ensures robust training and testing. Experimental results show that the Time-Sequence GRNN achieves the lowest mean squared error (MSE) loss, outperforming traditional neural networks. This method significantly enhances sensor calibration for USVs, promising improved data accuracy in challenging maritime conditions. Future work will refine the network and validate results with real-world data.