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：本文提出一种新颖的架构，旨在语义感知巡检任务期间增量式构建并利用分层三维场景图表征。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、导航与场景理解过程中利用场景语义结构提供了契机。基于此，我们提出三维分层语义图（3DLSG），这是一种以增量方式构建的分层巡检场景图，其按抽象层级组织以实时支持规划需求。针对语义感知巡检任务，我们提出名为增强型初检-巡检-探索（xFLIE）的任务框架，该框架将3DLSG与巡检规划器紧密耦合。我们通过仿真与实验验证评估了基于3DLSG模型的目标选择、路径规划及语义导航任务性能。所呈现的场景具有多样性，涵盖模拟环境中城市尺度的分布式目标与孤立基础设施目标，以及后续在四足机器人上实施的户外与地下环境部署。所提方法成功展示了基于3DLSG表征的增量式构建与规划能力，能够有效达成任务目标。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，相较于传统基于体素地图的方法，路径规划时间实现了数量级的缩减，证明了所提方法的规划效率与可扩展性。