Bayesian filtering for localization using decoupled visual measurements

In this paper, we present a particle-filter-based localization framework with decoupled visual measurements (image features) for process and measurement models. Thus our approach enables using camera-based motion estimation while achieving the independence between the process noise and the measurement noise in the Bayesian filtering framework. In addition, we alternately perform sequential and global localization on the basis of the marginal likelihood in order to avoid severe errors caused by incorrect data association.