Path planning is a basic capability of autonomous mobile robots. Former approaches in path planning exploit only the given geometric information from the environment without leveraging the inherent semantics within the environment. The recently presented S-Graphs constructs 3D situational graphs incorporating geometric, semantic, and relational aspects between the elements to improve the overall scene understanding and the localization of the robot. But these works do not exploit the underlying semantic graphs for improving the path planning for mobile robots. To that aim, in this paper, we present S-Nav a novel semantic-geometric path planner for mobile robots. It leverages S-Graphs to enable fast and robust hierarchical high-level planning in complex indoor environments. The hierarchical architecture of S-Nav adds a novel semantic search on top of a traditional geometric planner as well as precise map reconstruction from S-Graphs to improve planning speed, robustness, and path quality. We demonstrate improved results of S-Nav in a synthetic environment.

翻译：路径规划是自主移动机器人的基本能力。传统路径规划方法仅利用环境中给定的几何信息，未挖掘环境中固有的语义信息。近期提出的S-Graphs方法通过构建三维情境图，融合元素间的几何、语义及关系特征，提升了整体场景理解与机器人定位能力，但此类研究未利用底层语义图优化移动机器人路径规划。为此，本文提出面向移动机器人的新型语义-几何路径规划器S-Nav。该方法借助S-Graphs，在复杂室内环境中实现快速鲁棒的分层高层规划。S-Nav的分层架构在传统几何规划器基础上新增语义搜索层，并通过S-Graphs实现精确地图重建，从而提升规划速度、鲁棒性与路径质量。我们在合成环境中验证了S-Nav的改进效果。