Robot Exploration by Subjectively Maximizing Objective Information Gain

Localization, mapping and action selection are three main aspects in robot exploration. This paper proposes an autonomous exploration method for robot localization and mapping in unknown environments. First an ideal global-probabilistic measure, which we call objective objective function, is introduced to evaluate the objective exploration performance. By minimizing a local approximation of this measure (which we call subjective objective function) the robot learns the internal models, and achieves a consistent correlation between the internal representation and the reality. Furthermore, an action policy search method is used to learn the optimal action selection strategy by maximizing the information gain obtained in exploration. Simulation results demonstrate that the proposed framework provides an integrated solution for localization and mapping task in unstructured environments.