Title: Theoretical Data-Driven MobilePosenet: Lightweight Neural Network for Accurate Calibration-Free 5-DOF Magnet Localization Show Chinese title

Title: 理论数据驱动MobilePosenet：用于无标定准确5-DOF磁定位的轻量级神经网络

Abstract: 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. Show Chinese abstract

Abstract: 使用外部传感器阵列的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计算延迟和需要初始位置估计的挑战。 最近提出的基于神经网络的方法通常需要大量的硬件校准和真实数据收集，这些过程耗时且劳动密集。 为了解决这些挑战，我们提出了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，网络的输入集成了传感器的坐标信息和随机噪声，弥补了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\(90 \times 90 \times 80\) mm的工作空间中进行的实验评估表明，MobilePosenet在与基于真实数据训练的最先进方法相比时，表现出优异的5-DOF定位精度（$1.54 \pm 1.03$ mm和$2.24 \pm 1.84^{\circ}$）和推理速度（0.9 ms）。 由于网络训练仅依赖于理论数据，MobilePosenet可以消除硬件校准和真实数据收集过程，提高这种永磁体定位方法的泛化能力，并提升在不同临床环境中的快速应用潜力。