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Situationally-Aware Path Planning Exploiting 3D Scene Graphs
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DOI：10.1109/LRA.2026.3656775.png)
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摘要
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3D Scene Graphs integrate both metric and semantic information, yet their structure remains underexploited 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 re-use 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 6× 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.
Keyword:
Motion and path planning
3D scene graphs
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5.3
论文数:
1.9K
被引数:
3.9W
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