  The paper proposes a reliable and robust planning solution to the long range
robotic navigation problem in extremely cluttered environments. A two-layer
planning architecture is proposed that leverages both the environment map and
the direct depth sensor information to ensure maximal information gain out of
the onboard sensors. A frontier-based pose sampling technique is used with a
fast marching cost-to-go calculation to select a goal pose and plan a path to
maximize robot exploration rate. An artificial potential function approach,
relying on direct depth measurements, enables the robot to follow the path
while simultaneously avoiding small scene obstacles that are not captured in
the map due to mapping and localization uncertainties. We demonstrate the
feasibility and robustness of the proposed approach through field deployments
in a structurally complex warehouse using a micro-aerial vehicle (MAV) with all
the sensing and computations performed onboard.
