xFLIE：利用可操作的层次场景表示进行自主语义感知检查任务
xFLIE: Leveraging Actionable Hierarchical Scene Representations for Autonomous Semantic-Aware Inspection Missions
AI总结：
xFLIE通过3DLSG实现自主语义感知检查任务的高效路径规划和导航
摘要：
我们提出了一种新的架构，旨在在语义感知检查任务中逐步构建和利用层次化的3D场景图表示。检查规划，特别是对以前未见过环境中分布目标的规划，提供了一个机会，在推理、导航和场景理解过程中利用场景的语义结构。受此启发，我们提出了3D分层语义图（3DLSG），这是一种层次化的检查场景图，以增量方式构建，并组织成抽象层次，以支持实时的规划需求。为了解决语义感知检查任务，我们提出了一个称为增强首次查看检查探索（xFLIE）的任务框架，该框架紧密耦合3DLSG与检查规划器。我们通过模拟和实验测试来评估性能，评估在3DLSG模型上目标选择、路径规划和语义导航任务的表现。所呈现的场景多样，从城市规模的分布式到模拟世界中的孤立基础设施目标，以及随后在四足机器人上的户外和地下环境部署。所提出的方法成功展示了在3DLSG表示上逐步构建和规划以满足任务目标的能力。此外，该框架在检查任务结束时在结构接口上成功完成了语义导航任务。最后，我们报告了在各种环境规模上相比传统体素图方法，路径规划时间减少了多个数量级，证明了所提出方法的规划效率和可扩展性。
英文摘要：
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.
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