Memory-Based State-Estimation

In this paper we introduce a state-estimation method that uses a short-term memory to calculate the current state. A common way to solve state estimation problems is to use implementations of the Bayesian algorithm like Kalman filters or particle filters. When implementing a Bayesian filter several problems can arise. One difficulty is to obtain error models for the sensors and for the state transitions. The other difficulty is to find an adequate compromise between the accuracy of the belief probability distribution and the computational costs that are needed to update it. In this paper we show how a short-term memory of perceptions and actions can be used to calculate the state. In contrast to the Bayesian filter, this method does not need an internal representation of the state which is updated by the sensor and motion information. It is shown that this is especially useful when information of sparse sensors (sensors with non-unique measurements with respect of the state) has to be integrated.