Online estimation of variance parameters: Experimental results with applications to localization

This paper presents an experimental validation of an online estimation algorithm we recently investigated theoretically. One of the peculiar characteristics of the approach we propose is the ability to perform an online estimation of the variance parameters that regulate the dynamics of the nonlinear dynamical model used. The approach exploits and extends classical iterated Kalman filtering equations by propagating an approximation of the marginal posterior of the unknown variances over time. The method has been previously used to model and solve a localization task for multiple robots equipped only with a sensor returning mutual distances. In this paper we present a first experimental validation of the algorithm that complements and confirms our initial promising theoretical findings. Our current implementation relies on a sensor returning distance estimates based on a simple image processing algorithm. Such sensor is inherently and intentionally noisy, and in this study we show that our technique is capable of appropriately estimating the variance describing the noise affecting this sensor. We conclude proving experimentally that the procedure we present ensures a performance comparable to similar algorithms that require significantly more a priori information.