A Robust Magnetic Tracking Approach Based on Graph Optimization

Magnetic tracking approach (MTA) is mainly based on the observation of the magnetic field produced by a magnetic tracking target. A permanent magnet is usually employed as the tracking target, and a magnetic dipole model is used to estimate the magnetic field. In this article, we introduce an intuitive method to solve the magnetic tracking problem based on graph optimization. A graph is constructed to formulate the tracking problem, whose nodes correspond to the poses of the permanent magnet at different times and whose edges represent constraints derived by sensor measurements. The accuracy and robustness of MTA play a vital role in biomedical and industrial applications. However, a potential abnormal edge will appear in the graph when a magnetic sensor is disturbed by an interference source like a small magnet, which will deteriorate the localization accuracy of MTA. To improve its robustness, Huber cost function is used to reduce the weight of the abnormal edge. Finally, experiments were carried out to verify the performance of the proposed approach with and without magnetic field interference. Comparing the localization errors with the MTA based on the standard Levenberg–Marquardt algorithm, the results illustrate that the proposed approach could achieve superior localization accuracy and anti-interference ability.