Towards Real-Time Global Localization in Dynamic Unstructured Environments

Global localization is the problem in which a mobile robot has to estimate the self-position with respect to an a priori given map as it navigates without using any a priori knowledge of the initial self-position. Previous studies on global localization mainly focused on static environments, where the a priori map is almost correct. On the other hand, in dynamic environments, there are several sources of computational complexity. For example, not only the self-position but also the map should be estimated due to the map errors. The main contribution of this paper is to address such computational complexity by decomposing our global localization problem into two smaller subproblems, and solving the subproblems in a practical computation time. Also, we demonstrate the robustness and the efficiency of the proposed method in various large and complex environments.