  This paper contributes a novel strategy for semantics-aware autonomous
exploration and inspection path planning. Attuned to the fact that environments
that need to be explored often involve a sparse set of semantic entities of
particular interest, the proposed method offers volumetric exploration combined
with two new planning behaviors that together ensure that a complete mesh model
is reconstructed for each semantic, while its surfaces are observed at
appropriate resolution and through suitable viewing angles. Evaluated in
extensive simulation studies and experimental results using a flying robot, the
planner delivers efficient combined exploration and high-fidelity inspection
planning that is focused on the semantics of interest. Comparisons against
relevant methods of the state-of-the-art are further presented.
