2023
DOI: 10.1109/tii.2022.3233675
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AMagPoseNet: Real-Time Six-DoF Magnet Pose Estimation by Dual-Domain Few-Shot Learning From Prior Model

Abstract: Traditional magnetic tracking approaches based on mathematical models and optimization algorithms are computationally intensive, depend on initial guesses, and do not guarantee convergence to a global optimum. Although fully supervised data-driven deep learning can solve the above issues, the demand for a comprehensive dataset hampers its applicability in magnetic tracking. Thus, we propose an annular magnet pose estimation network (called AMagPoseNet) based on dual-domain few-shot learning from a prior mathem… Show more

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Cited by 9 publications
(2 citation statements)
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References 28 publications
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“…[16][17][18][19] Su et al introduced AMagPoseNet, an end-to-end neural network model designed to tackle the inverse static magnetic problem. 20 Chen et al designed a MagX sensor matrix for the positioning of wearable spherical magnets. The system is capable of simultaneously locating multiple permanent magnets with low power consumption, achieving an average error of 7.4 mm in tracking magnetic capsule.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…[16][17][18][19] Su et al introduced AMagPoseNet, an end-to-end neural network model designed to tackle the inverse static magnetic problem. 20 Chen et al designed a MagX sensor matrix for the positioning of wearable spherical magnets. The system is capable of simultaneously locating multiple permanent magnets with low power consumption, achieving an average error of 7.4 mm in tracking magnetic capsule.…”
Section: Related Workmentioning
confidence: 99%
“…There are primarily two types of magnetic sources for spatial localization, that is, solenoid coils 14,15 and permanent magnets 16–19 . Su et al introduced AMagPoseNet, an end‐to‐end neural network model designed to tackle the inverse static magnetic problem 20 . Chen et al designed a MagX sensor matrix for the positioning of wearable spherical magnets.…”
Section: Related Workmentioning
confidence: 99%