Uncertainty-aware receding horizon exploration and mapping using aerial robots

This paper presents a novel path planning algorithm for autonomous, uncertainty-aware exploration and mapping of unknown environments using aerial robots. The proposed planner follows a two-step, receding horizon, belief space-based approach. At first, in an online computed tree the algorithm finds the branch that optimizes the amount of space expected to be explored. The first viewpoint configuration of this branch is selected, but the path towards it is decided through a second planning step. Within that, a new tree is sampled, admissible branches arriving at the reference viewpoint are found and the robot belief about its state and the tracked landmarks of the environment is propagated. The branch that minimizes the expected localization and mapping uncertainty is selected, the corresponding path is executed by the robot and the whole process is iteratively repeated. The proposed planner is capable of running online onboard a small aerial robot and its performance is evaluated using experimental studies in a challenging environment.