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

arXiv: https://arxiv.org/abs/2406.04821 · HTML: https://mlsanity.com/paper/2406.04821/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2024.

## Built on by (most cited first, 9 of 9)

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- [Dynamic Optimization of Storage Systems Using Reinforcement Learning Techniques](https://mlsanity.com/paper/2501.00068/) (2024)
- [Innovative Deep Learning Techniques for Obstacle Recognition: A Comparative Study of Modern Detection Algorithms](https://mlsanity.com/paper/2410.10096/) (2024)
- [Cutting-Edge Detection of Fatigue in Drivers: A Comparative Study of Object Detection Models](https://mlsanity.com/paper/2410.15030/) (2024)
- [Construction and optimization of health behavior prediction model for the elderly in smart elderly care](https://mlsanity.com/paper/2412.02062/) (2024)

## Abstract (arXiv)

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.