Automatic Calibration of Magnetic Tracking
Mingke Wang, Qing Luo, Yasha Iravantchi, Xiaomeng Chen, Alanson Sample, Kang G. Shin, Xiaohua Tian, Xinbing Wang, Dongyao Chen
Magnetic sensing is emerging as an enabling technology for various engaging applications. Representative use cases include high-accuracy posture tracking, human-machine interaction, and haptic sensing. This technology uses multiple MEMS magnetometers to capture the changing magnetic field at a close distance. However, magnetometers are susceptible to real-world disturbances, such as hard- and soft-iron effects. As a result, users need to perform a cumbersome and lengthy calibration process frequently, severely limiting the usability of magnetic tracking. To remove/mitigate this limitation, we propose MAGIC (MAGnetometer automatIc Calibration), a systematic framework to automatically calibrate both soft- and hard-iron disturbances for a MEMS magnetometer array. To minimize the need for user intervention, we introduce a novel auto-triggering module. Unlike the legacy manual calibration method, MAGIC achieves superior calibration performance (e.g., for tracking applications) with minimal user attention. Via empirical studies, we show MAGIC also incurs marginal overhead and cost, such as a total energy cost of 0.108 J.