artificial intelligence; deep learning; real-time system

Traditional magnetic localization methods based on mathematical model and optimization algorithm often fail to achieve the global optimum due to their dependency on initial values of pose parameters. Despite deep learning can potentially address these limitations, the existing methods are restricted to capture both global position distribution and local spatial features, leading to diminished performance in tasks required precise pose information.