Maximum Likelihood Identification of Inertial Sensor Noise Model Parameters

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Accurate visual-inertial localization and mapping systems require accurate calibration and good sensor error models. To this end, we present a simple offline method to automatically determine the parameters of inertial sensor noise models. The proposed methodology identifies noise processes across a large range of strength and time-scales, for example, weak gyroscope bias fluctuations buried in broadband noise. This is accomplished with a classical maximum likelihood estimator, based on the integrated process (i.e., the angle, velocity, or position), rather than on the angular rate or acceleration as is standard in the literature. This trivial modification allows us to capture noise processes according to their effect on the integrated process, irrespective of their contribution to rate or acceleration noise. The cause of the noise is not discussed in this article. The method is tested on different classes of sensors by automatically identifying the parameters of a standard inertial sensor noise model. The results are analyzed qualitatively by comparing the model’s Allan variance to the Allan variance computed directly from sensor data. A simulation that resembles one of the devices under test facilitates a quantitative analysis of the proposed estimator. Comparison with a competing, state-of-the-art method shows the advantages of the algorithm.