A Computationally Efficient Framework for Automatic Inertial Sensor Calibration

The calibration of (low-cost) inertial sensors has become increasingly important over the past years, since their use has grown exponentially in many applications going from unmanned aerial vehicle navigation to 3-D animation. However, this calibration procedure is often quite problematic since, aside from compensating for deterministic measurement errors due to physical phenomena such as dynamics or temperature, the stochastic signals issued from these sensors in static settings have a complex spectral structure and the methods available to estimate the parameters of these models are either unstable, computationally intensive, and/or statistically inconsistent. This paper presents a new software platform for calibration of the stochastic component in inertial sensor measurement errors based on the generalized method of wavelet moments, which provides a computationally efficient, flexible, user-friendly, and statistically sound tool to estimate and select from a wide range of complex models. In addition, all this is possible also in a robust framework allowing to perform sensor calibration when the data are affected by outliers. The software is developed within the open-source statistical software R and is based on C++ language allowing it to achieve high computational performance.