Autonomous navigation missions require online decision making abilities, in order to choose from a given set of candidate actions an action that will lead to the best outcome. In a partially observable setting, decision making under uncertainty, also known as belief space planning (BSP), involves reasoning about belief evolution considering realizations of future observations. Yet, when  candidate actions lead the robot to an unknown environment the decision making mission becomes a very challenging problem since without a map it is hard to foresee future observations.

In this thesis we develop a data-driven approach for predicting a distribution over an unexplored map, generating future observations, and combining these observations within BSP. We examine our approach and compare it to existing BSP methods in a Gazebo simulation, and demonstrate it often yields improved performance.