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Real-Time 5-DOF Object Pose Estimation Using Uniaxial Magnetic Sensor Array and MPNet Convolutional Network.

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Bibliographic Details
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]
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Database: Engineering Source
Description
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]
ISSN:01291564
DOI:10.1142/S0129156425406473