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.

翻译：本文提出了一种用于语义感知自主探索与检测路径规划的新策略。针对待探索环境通常包含少量特别关注的语义实体这一事实，所提方法结合了体素探索与两种新的规划行为，共同确保为每个语义实体重建完整的网格模型，同时以适当分辨率和合适的观察角度观测其表面。通过飞行机器人进行的大量仿真研究与实验评估表明，该规划器能够实现高效的联合探索与高保真检测规划，且重点聚焦于感兴趣的语义对象。此外，本文还呈现了与现有先进方法的对比分析。