Real-Time 5-DOF Object Pose Estimation Using Uniaxial Magnetic Sensor Array and MPNet Convolutional Network.
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| Title: | Real-Time 5-DOF Object Pose Estimation Using Uniaxial Magnetic Sensor Array and MPNet Convolutional Network. |
|---|---|
| Authors: | Zhou, Ke1 (AUTHOR), Mo, Zhiyue1 (AUTHOR), Lu, Baihua1 (AUTHOR), Qin, Si1 (AUTHOR), Jin, Qingren1 (AUTHOR) zha567126@163.com |
| Source: | International Journal of High Speed Electronics & Systems. Aug2026, Vol. 35 Issue 3, p1-18. 18p. |
| Subjects: | Magnetic sensors, Pose estimation (Computer vision), Acquisition of data, Convolutional neural networks, Artificial neural networks, Magnetic flux density, Data augmentation |
| Abstract: | With advancements in intelligent technologies, near-field pose estimation has become essential across various applications. This paper presents a 5-degree-of-freedom (DOF) real-time object pose estimation system based on a uniaxial magnetic sensor array and MPNet convolutional network. A simulated dataset of magnetic field distributions is created for training, utilizing a data augmentation method that reduces pose space traversal by up to 98.43%. The SMPNet network, built on a ResNet backbone, achieves high classification accuracy and Pearson correlation for object pose estimation. A multi-channel data acquisition system with an 8 ×8 sensor array is designed, and real-world data are used to optimize the model, achieving a mean square error of 2.02 mm for XYZ displacement and 5.92 ∘ for rotational angles. The system is integrated with hardware to minimize data transmission delays, achieving a total system power consumption of 2.058 W and an estimated pose RMS error of 2.38 mm and 7.56 ∘ . This study demonstrates the feasibility of using a uniaxial magnetic array for real-time pose estimation, offering insights for future system optimizations and practical applications. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of High Speed Electronics & Systems is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192030662 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Real-Time 5-DOF Object Pose Estimation Using Uniaxial Magnetic Sensor Array and MPNet Convolutional Network. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhou%2C+Ke%22">Zhou, Ke</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mo%2C+Zhiyue%22">Mo, Zhiyue</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lu%2C+Baihua%22">Lu, Baihua</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qin%2C+Si%22">Qin, Si</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jin%2C+Qingren%22">Jin, Qingren</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zha567126@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+High+Speed+Electronics+%26+Systems%22">International Journal of High Speed Electronics & Systems</searchLink>. Aug2026, Vol. 35 Issue 3, p1-18. 18p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Magnetic+sensors%22">Magnetic sensors</searchLink><br /><searchLink fieldCode="DE" term="%22Pose+estimation+%28Computer+vision%29%22">Pose estimation (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Acquisition+of+data%22">Acquisition of data</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+flux+density%22">Magnetic flux density</searchLink><br /><searchLink fieldCode="DE" term="%22Data+augmentation%22">Data augmentation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: With advancements in intelligent technologies, near-field pose estimation has become essential across various applications. This paper presents a 5-degree-of-freedom (DOF) real-time object pose estimation system based on a uniaxial magnetic sensor array and MPNet convolutional network. A simulated dataset of magnetic field distributions is created for training, utilizing a data augmentation method that reduces pose space traversal by up to 98.43%. The SMPNet network, built on a ResNet backbone, achieves high classification accuracy and Pearson correlation for object pose estimation. A multi-channel data acquisition system with an 8 ×8 sensor array is designed, and real-world data are used to optimize the model, achieving a mean square error of 2.02 mm for XYZ displacement and 5.92 ∘ for rotational angles. The system is integrated with hardware to minimize data transmission delays, achieving a total system power consumption of 2.058 W and an estimated pose RMS error of 2.38 mm and 7.56 ∘ . This study demonstrates the feasibility of using a uniaxial magnetic array for real-time pose estimation, offering insights for future system optimizations and practical applications. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of High Speed Electronics & Systems is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1142/S0129156425406473 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 1 Subjects: – SubjectFull: Magnetic sensors Type: general – SubjectFull: Pose estimation (Computer vision) Type: general – SubjectFull: Acquisition of data Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Magnetic flux density Type: general – SubjectFull: Data augmentation Type: general Titles: – TitleFull: Real-Time 5-DOF Object Pose Estimation Using Uniaxial Magnetic Sensor Array and MPNet Convolutional Network. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhou, Ke – PersonEntity: Name: NameFull: Mo, Zhiyue – PersonEntity: Name: NameFull: Lu, Baihua – PersonEntity: Name: NameFull: Qin, Si – PersonEntity: Name: NameFull: Jin, Qingren IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 01291564 Numbering: – Type: volume Value: 35 – Type: issue Value: 3 Titles: – TitleFull: International Journal of High Speed Electronics & Systems Type: main |
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