利用3D场景图提升路径规划效率与可解释性
Situationally-aware Path Planning Exploiting 3D Scene Graphs
- 分两阶段规划：先语义搜索生成可理解路径
- 复杂场景下规划时间减少6倍，效果优于传统方法
- 适合需要高效可解释路径的室内导航系统
3D场景图融合了度量与语义信息，但其结构在路径规划中仍未被充分利用。本文提出S-Path，一种情境感知的路径规划器，通过挖掘室内3D场景图中的度量-语义结构，显著提升规划效率。S-Path采用两阶段流程：首先基于场景图生成的语义图进行搜索，获得人类可理解的高层路径，并识别出相关规划区域，从而将问题分解为多个可并行求解的子问题。此外，引入重规划机制，在路径不可行时复用先前子问题信息，更新语义启发式策略，优先复用以进一步提升后续规划效率。在真实与仿真环境中的大量实验表明，S-Path平均将规划时间缩短6倍，且在路径最优性上与经典采样型规划器相当，在复杂场景中更优，适用于由室内3D场景图表示的环境。代码已开源：https://github.com/snt-arg/spath_ros。
原文摘要 · Abstract (English)
3D Scene Graphs integrate both metric and semantic information, yet their structure remains underutilized for improving path planning efficiency and interpretability. In this work, we present S-Path, a situationally-aware path planner that leverages the metric-semantic structure of indoor 3D Scene Graphs to significantly enhance planning efficiency. S-Path follows a two-stage process: it first performs a search over a semantic graph derived from the scene graph to yield a human-understandable high-level path. This also identifies relevant regions for planning, which later allows the decomposition of the problem into smaller, independent subproblems that can be solved in parallel. We also introduce a replanning mechanism that, in the event of an infeasible path, reuses information from previously solved subproblems to update semantic heuristics and prioritize reuse to further improve the efficiency of future planning attempts. Extensive experiments on both real-world and simulated environments show that S-Path achieves average reductions of 6x in planning time while maintaining comparable path optimality to classical sampling-based planners and surpassing them in complex scenarios, making it an efficient and interpretable path planner for environments represented by indoor 3D Scene Graphs. Code available at: https://github.com/snt-arg/spath_ros
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