Efficient entropy-based action selection for appearance-based robot localization

In this paper, we extend our appearance-based localization system moving from a passive approach to an active one where the robot can execute actions with the only purpose of gaining information about its location in the environment. We present a general framework for entropy-based action selection and we describe how this framework can be implemented in our localization system. The result is an action evaluation process more efficient in memory and in execution time than previously existing ones. The experiments we present show that the action selection mechanism effectively decreases the error on localization in environments with a high degree of aliasing. This can be of great help to improve the performance of our localization system in dynamic environments.