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.

翻译：利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）算法的传统跟踪方法面临计算延迟和需要初始位置估计的挑战。近期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世界数据采集，过程耗时费力。为应对这些挑战，我们提出了MobilePosenet——一种轻量级神经网络架构，其利用深度可分离卷积以最小化计算成本，并采用通道注意力机制以提升定位精度。此外，网络输入整合了传感器坐标信息与随机噪声，补偿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 工作空间内进行的实验评估表明，相较于基于真实数据训练的最先进方法，MobilePosenet展现出优异的五自由度定位精度（位置误差 $1.54 \pm 1.03$ mm，姿态误差 $2.24 \pm 1.84^{\circ}$）和推理速度（0.9 ms）。由于网络训练仅依赖理论数据，MobilePosenet能够免除硬件校准和真实世界数据采集过程，从而提升了该永磁体定位方法的泛化能力，并增强了其在不同临床场景中快速应用的潜力。