This paper presents a novel approach to deal with nonlinear filtering by augmenting an Unscented Kalman Filter (UKF) with an Optimal quantization algorithm, named OQ-UKF. The Unscented Kalman Filter uses a sigma-point based method to approximate the distribution of an unknown random variable onto which is applied a nonlinear transformation, providing a cloud of evolving points. However, the generation of these socalled sigma-points is done by a deterministic algorithm which needs tuning in order to accurately capture the distribution of the estimate. This tuning is often problem-dependent due to nonlinearities and sometimes not optimal. We propose to fuse an UKF with Optimal quantization whose objective is to find the best approximation of the density of a random variable. The designed OQ-UKF is described in this paper, and its performance is evaluated for some relevant practical problems, such as pose estimation of a two-dimensional mobile robot.