Stochastic cloning: A generalized framework for processing relative state measurements

This paper introduces a generalized framework, termed "stochastic cloning," for processing relative state measurements within a Kalman filter estimator. The main motivation and application for this methodology is the problem of fusing displacement measurements with position estimates for mobile robot localization. Previous approaches have ignored the developed interdependencies (cross-correlation terms) between state estimates of the same quantities at different time instants. By directly expressing relative state measurements in terms of previous and current state estimates, the effect of these cross-correlation terms on the estimation process is analyzed and considered during updates. Simulation and experimental results validate this approach.