Probabilistic localization for mobile robots using incomplete maps

This paper addresses the problem of global localization for mobile robots in changeable environments, i.e. to estimate self-position using a map that is partially or completely different from the environment. It is difficult to detect changes when both of the self-position and the map have large uncertainties. To solve the problem, in this paper, we extend Monte Carlo localization (MCL) and sensor resetting localization (SRL), so as to generate a number of hypotheses about the change as well as the self-position. As a result of tests in a number of environments as well as changes, we found the proposed method is effective even when "rate of changes (ROC)" is high in the environment.