Global localization with detection of changes in non-stationary environments

In this paper, we propose a method for global localization in non-stationary environments, where the environment is partially or completely different from the map. We assume there is no moving object. It is difficult to detect changes when both of the self-position and the map have large uncertainties. To solve the problem, we extended Monte Carlo Localization (MCL) so as to generate a number of hypotheses about the change as well as the self-position. We also introduced Sensor Resetting Localization (SRL), in order to generate initial estimation of self-position, or to recover from large positioning errors. The proposed method has been tested in a number of environments as well as changes. As a results, we found the proposed method is effective even when "Rate Of Changes (ROC)" is high in the environment.