一种增强未知环境中自主巡检任务的可执行分层场景表示方法
An Actionable Hierarchical Scene Representation Enhancing Autonomous Inspection Missions in Unknown Environments
AI总结：
本研究提出与FLIE规划器集成的分层语义图LSG，通过嵌套分层图与实时语义分割实现多分辨率场景表示与分层规划，经仿真与Spot机器人户外实验验证，可增强未知环境自主巡检能力并提升操作员态势感知。
摘要：
本文提出了分层语义图（Layered Semantic Graphs，LSG）——一种新型可执行分层场景图，它与多模态任务规划器FLIE（基于初查的巡检与探索规划器，A First-Look based Inspection and Exploration planner）完全集成。本研究的创新点在于，致力于解决在未知环境中对先验未知的感兴趣目标执行持续巡检任务时，维护直观且多分辨率的场景表示，同时为规划与场景理解提供可处理基础的问题。所提出的LSG方案由多层抽象级别的局部嵌套分层图构成，其中的抽象概念以集成的FLIE规划器的功能为基础。此外，LSG封装了实时语义分割模型，可在分层表示中提取并定位所需的语义元素。这拓展了巡检规划器的能力，使其能够利用LSG做出知情决策，对特定的感兴趣语义对象进行巡检。本文还强调了LSG的分层语义路径规划能力，该能力可通过提升未知环境中人类操作员的态势感知能力，拓展巡检任务的范围。我们通过在仿真环境中对所提架构进行大量评估，以及在城市户外环境中部署波士顿动力Spot四足机器人开展现场实验，验证了所提方案的有效性。
英文摘要：
In this article, we present the Layered Semantic Graphs (LSG), a novel actionable hierarchical scene graph, fully integrated with a multi-modal mission planner, the FLIE: A First-Look based Inspection and Exploration planner. The novelty of this work stems from aiming to address the task of maintaining an intuitive and multi-resolution scene representation, while simultaneously offering a tractable foundation for planning and scene understanding during an ongoing inspection mission of apriori unknown targets-of-interest in an unknown environment. The proposed LSG scheme is composed of locally nested hierarchical graphs, at multiple layers of abstraction, with the abstract concepts grounded on the functionality of the integrated FLIE planner. Furthermore, LSG encapsulates real-time semantic segmentation models that offer extraction and localization of desired semantic elements within the hierarchical representation. This extends the capability of the inspection planner, which can then leverage LSG to make an informed decision to inspect a particular semantic of interest. We also emphasize the hierarchical and semantic path-planning capabilities of LSG, which could extend inspection missions by improving situational awareness for human operators in an unknown environment. The validity of the proposed scheme is proven through extensive evaluations of the proposed architecture in simulations, as well as experimental field deployments on a Boston Dynamics Spot quadruped robot in urban outdoor environment settings.
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