A stochastic filter for planar rigid-body motions

This paper presents a novel algorithm for the estimation of planar rigid-body motions. It is based on using a probability distribution that is inherently defined on the nonlinear manifold representing these motions and on proposing a deterministic sampling scheme that makes consideration of complicated system models possible. Furthermore, we show that the measurement update for a manifold equivalent to noisy direct measurements can be carried out in closed form. Thus, the resulting method avoids errors made due to local linearization and outperforms methods that wrongly assume Gaussian distributions, which we show by comparing the proposed filter to the UKF.