In this paper we address smoothing-that is, optimisation-based-estimation techniques for localisation problems in the case where motion sensors are very accurate. Our mathematical analysis focuses on the difficult limit case where motion sensors are infinitely precise, resulting in the absence of process noise. Then the formulation degenerates, as the dynamical model that serves as a soft constraint becomes an equality constraint, and conventional smoothing methods are not able to fully respect it. By contrast, once an appropriate Lie group embedding has been found, we prove theoretically that invariant smoothing gracefully accommodates this limit case in that the estimates tend to be consistent with the induced constraints when the noise tends to zero. Simulations on the important problem of initial alignement in inertial navigation show that, in a low noise setting, invariant smoothing may favorably compare to state-of-the-art smoothers when using precise inertial measurements units (IMU).
In this paper we address smoothing-that is, optimisation-based-estimation techniques for localisation problems in the case where motion sensors are very accurate. Our mathematical analysis focuses on the difficult limit case where motion sensors are infinitely precise, resulting in the absence of process noise. Then the formulation degenerates, as the dynamical model that serves as a soft constraint becomes an equality constraint, and conventional smoothing methods are not able to fully respect it. By contrast, once an appropriate Lie group embedding has been found, we prove theoretically that invariant smoothing gracefully accommodates this limit case in that the estimates tend to be consistent with the induced constraints when the noise tends to zero. Simulations on the important problem of initial alignement in inertial navigation show that, in a low noise setting, invariant smoothing may favorably compare to state-of-the-art smoothers when using precise inertial measurements units (IMU).