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:本文提出了一种具有语义意识的自主勘探和检查路径规划的新战略,考虑到需要探索的环境往往涉及一组特别感兴趣的少数语义实体,拟议方法提供了量式勘探,加上两种新的规划行为,共同确保为每个语义重建完整的网目模型,同时以适当的分辨率和适当的观察角度观察其表面。在广泛的模拟研究和实验结果中,利用飞行机器人进行了评价,规划员提供了高效的混合勘探和高度忠诚的视察规划,重点是利息的语义。还进一步介绍了与最新技术相关方法的比较。</s>