We present a novel architecture aimed towards incremental construction and exploitation of a hierarchical 3D scene graph representation during semantic-aware inspection missions. Inspection planning, particularly of distributed targets in previously unseen environments, presents an opportunity to exploit the semantic structure of the scene during reasoning, navigation and scene understanding. Motivated by this, we propose the 3D Layered Semantic Graph (3DLSG), a hierarchical inspection scene graph constructed in an incremental manner and organized into abstraction layers that support planning demands in real-time. To address the task of semantic-aware inspection, a mission framework, termed as Enhanced First-Look Inspect Explore (xFLIE), that tightly couples the 3DLSG with an inspection planner is proposed. We assess the performance through simulations and experimental trials, evaluating target-selection, path-planning and semantic navigation tasks over the 3DLSG model. The scenarios presented are diverse, ranging from city-scale distributed to solitary infrastructure targets in simulated worlds and subsequent outdoor and subterranean environment deployments onboard a quadrupedal robot. The proposed method successfully demonstrates incremental construction and planning over the 3DLSG representation to meet the objectives of the missions. Furthermore, the framework demonstrates successful semantic navigation tasks over the structured interface at the end of the inspection missions. Finally, we report multiple orders of magnitude reduction in path-planning time compared to conventional volumetric-map-based methods over various environment scale, demonstrating the planning efficiency and scalability of the proposed approach.

翻译：本文提出了一种新颖的架构，旨在语义感知巡检任务中增量式构建并利用分层三维场景图表示。巡检规划，特别是在未知环境中对分布式目标进行巡检，为在推理、导航和场景理解过程中利用场景的语义结构提供了契机。受此启发，我们提出了三维分层语义图（3DLSG），这是一种以增量方式构建的分层巡检场景图，其按抽象层次组织以实时支持规划需求。针对语义感知巡检任务，我们提出了一种任务框架——增强型先视巡检探索（xFLIE），该框架将3DLSG与巡检规划器紧密耦合。我们通过仿真和实验测试评估了该方法的性能，重点在3DLSG模型上评估了目标选择、路径规划和语义导航任务。所呈现的场景具有多样性，涵盖从模拟世界中城市尺度的分布式目标到孤立基础设施目标，以及后续在四足机器人上进行的户外和地下环境部署。所提方法成功演示了基于3DLSG表示的增量式构建与规划，以实现任务目标。此外，该框架在巡检任务结束时，通过结构化接口成功完成了语义导航任务。最后，我们报告了在不同环境尺度下，与传统的基于体素地图的方法相比，路径规划时间减少了多个数量级，证明了所提方法的规划效率和可扩展性。