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

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arxiv 2406.04821 v1 pith:75U6MJHP submitted 2024-06-07 cs.RO

Yi Shen , Hao Liu , Chang Zhou , Wentao Wang , Zijun Gao , Qi Wang This is my paper · ORCID
classification cs.RO
keywords dataaccuracycalibrationconditionsdeepdynamicextrinsicgrnn
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Forward citations

Cited by 6 Pith papers

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