论文标题
利用综合数据在监督学习中进行鲁棒的5-DOF磁标记定位
Utilizing Synthetic Data in Supervised Learning for Robust 5-DoF Magnetic Marker Localization
论文作者
论文摘要
跟踪被动磁标记在推进医疗保健和机器人技术方面起着至关重要的作用，从而有可能显着提高系统的精确度和效率。这项技术是开发更智能，更响应的工具和设备的关键，例如增强的手术仪器，精确的诊断工具以及具有提高环境互动功能的机器人。但是，传统上，由于对迭代优化程序的要求，磁标记的跟踪在计算上是昂贵的。此外，这些方法取决于其优化函数的磁偶极模型，由于该模型在处理非球形磁铁和传感器之间的短距离时，由于该模型的明显不准确而产生不精确的结果。我们的论文引入了一种新方法，该方法利用神经网络绕过这些限制，直接绕过这些标记的位置，而没有确定启动的元素，并确定了启动的元素。尽管我们的方法需要一个广泛的监督训练阶段，但我们通过引入一种更有效的方法来使用有限元方法模拟来生成合成但逼真的数据来减轻这种情况。快速准确推断的好处大大超过了离线训练准备。在我们的评估中，我们使用不同的圆柱磁铁，并使用16个传感器的正方形阵列跟踪。我们在便携式，面向神经网络的单板计算机上执行传感器的阅读和位置推断，以确保紧凑的设置。我们针对基于视觉的地面真实数据进行了基准测试，在0.2x0.2x0.15 m的工作量内达到了4 mm的平均位置误差和8度的方向误差。这些结果展示了我们的原型在跟踪5 DOF时有效平衡准确性和紧凑性的能力。
Tracking passive magnetic markers plays a vital role in advancing healthcare and robotics, offering the potential to significantly improve the precision and efficiency of systems. This technology is key to developing smarter, more responsive tools and devices, such as enhanced surgical instruments, precise diagnostic tools, and robots with improved environmental interaction capabilities. However, traditionally, the tracking of magnetic markers is computationally expensive due to the requirement for iterative optimization procedures. Moreover, these methods depend on the magnetic dipole model for their optimization function, which can yield imprecise outcomes due to the model's significant inaccuracies when dealing with short distances between non-spherical magnet and sensor.Our paper introduces a novel approach that leverages neural networks to bypass these limitations, directly inferring the marker's position and orientation to accurately determine the magnet's 5 DoF in a single step without initial estimation. Although our method demands an extensive supervised training phase, we mitigate this by introducing a computationally more efficient method to generate synthetic, yet realistic data using Finite Element Methods simulations. The benefits of fast and accurate inference significantly outweigh the offline training preparation. In our evaluation, we use different cylindrical magnets, tracked with a square array of 16 sensors. We perform the sensors' reading and position inference on a portable, neural networks-oriented single-board computer, ensuring a compact setup. We benchmark our prototype against vision-based ground truth data, achieving a mean positional error of 4 mm and an orientation error of 8 degrees within a 0.2x0.2x0.15 m working volume. These results showcase our prototype's ability to balance accuracy and compactness effectively in tracking 5 DoF.
