  In this work we propose a holistic framework for autonomous aerial inspection
tasks, using semantically-aware, yet, computationally efficient planning and
mapping algorithms. The system leverages state-of-the-art receding horizon
exploration techniques for next-best-view (NBV) planning with geometric and
semantic segmentation information provided by state-of-the-art deep
convolutional neural networks (DCNNs), with the goal of enriching environment
representations. The contributions of this article are threefold, first we
propose an efficient sensor observation model, and a reward function that
encodes the expected information gains from the observations taken from
specific view points. Second, we extend the reward function to incorporate not
only geometric but also semantic probabilistic information, provided by a DCNN
for semantic segmentation that operates in real-time. The incorporation of
semantic information in the environment representation allows biasing
exploration towards specific objects, while ignoring task-irrelevant ones
during planning. Finally, we employ our approaches in an autonomous drone
shipyard inspection task. A set of simulations in realistic scenarios
demonstrate the efficacy and efficiency of the proposed framework when compared
with the state-of-the-art.
