Mobile robot localization with an incomplete map in non-stationary environments

One of the fundamental problems of the mobile robots is self-localization, i.e., to estimate the self-position by comparing sensor data and a map. In non-stationary environments, a robot should avoid to use changed objects as landmarks in the localization. However, in most previous localization methods, it is assumed that there is no change, or changes are easily identified by sensing. In this paper, we propose a self-localization method that is robust against changes in environments. The method identifies changes from noisy and ambiguous sensor data. Since an object with a random shape may be added at a random position, it generates and utilizes multiple hypotheses about the changes. A number of simulation experiments have been performed in various environments, to demonstrate the effectiveness of the method.