Localizability estimation for mobile robots based on probabilistic grid map and its applications to localization

Zhe Liu, Weidong Chen, Yong Wang, Jingchuan Wang

Abstract: A novel approach to estimate localizability for mobile robots is presented based on probabilistic grid map (PGM). Firstly, a static localizability matrix is proposed for off-line estimation over the priori PGM. Then a dynamic localizability matrix is proposed to deal with unexpected dynamic changes. These matrices describe both localizability index and localizability direction quantitatively. The validity of the proposed method is demonstrated by experiments in different typical environments. Furthermore, two typical localization-related applications, including active global localization and pose tracking, are presented for illustrating the effectiveness of the proposed localizability estimation method.