理论数据驱动的 MobilePosenet：用于精确免校准 5-DOF 磁体定位的轻量级神经网络
Theoretical Data-Driven MobilePosenet: Lightweight Neural Network for Accurate Calibration-Free 5-DOF Magnet Localization
AI总结：
针对磁定位中传统算法延迟高、神经网络方法依赖硬件校准与真实数据的问题，提出仅用理论数据训练的轻量级 MobilePosenet，实现高精度、快速的免校准 5-DOF 永磁体定位。
摘要：
使用外部传感器阵列进行永磁体跟踪，对于无线胶囊内窥镜机器人的精确定位至关重要。基于磁偶极子模型和 Levenberg-Marquardt（LM）算法的传统跟踪算法面临与计算延迟以及需要初始位置估计相关的挑战。最近提出的基于神经网络的方法通常需要大量硬件校准和真实世界数据采集，这既耗时又耗费人力。为应对这些挑战，我们提出 MobilePosenet，这是一种轻量级神经网络架构，利用深度可分离卷积来最小化计算成本，并采用通道注意力机制来提高定位精度。此外，网络的输入整合了传感器的坐标信息和随机噪声，以补偿理论模型与实际磁场之间的差异，从而使 MobilePosenet 能够完全基于理论数据进行训练。在 90 × 90 × 80 mm 工作空间中进行的实验评估表明，与在真实世界数据上训练的最先进方法相比，MobilePosenet 表现出优异的 5-DOF 定位精度（1.54 ± 1.03 mm 和 2.24 ± 1.84°）和推理速度（0.9 ms）。由于网络训练仅依赖理论数据，MobilePosenet 可以省去硬件校准和真实世界数据采集过程，从而提高这种永磁体定位方法的泛化能力及其在不同临床场景中快速应用的潜力。
英文摘要：
Permanent magnet tracking using the external sensor array is crucial for the accurate localization of wireless capsule endoscope robots. Traditional tracking algorithms, based on the magnetic dipole model and Levenberg-Marquardt (LM) algorithm, face challenges related to computational delays and the need for initial position estimation. More recently proposed neural network-based approaches often require extensive hardware calibration and real-world data collection, which are time-consuming and labor-intensive. To address these challenges, we propose MobilePosenet, a lightweight neural network architecture that leverages depthwise separable convolutions to minimize computational cost and a channel attention mechanism to enhance localization accuracy. Besides, the inputs to the network integrate the sensors' coordinate information and random noise, compensating for the discrepancies between the theoretical model and the actual magnetic fields and thus allowing MobilePosenet to be trained entirely on theoretical data. Experimental evaluations conducted in a \(90 \times 90 \times 80\) mm workspace demonstrate that MobilePosenet exhibits excellent 5-DOF localization accuracy ($1.54 \pm 1.03$ mm and $2.24 \pm 1.84^{\circ}$) and inference speed (0.9 ms) against state-of-the-art methods trained on real-world data. Since network training relies solely on theoretical data, MobilePosenet can eliminate the hardware calibration and real-world data collection process, improving the generalizability of this permanent magnet localization method and the potential for rapid adoption in different clinical settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
