Situationally-aware Path Planning Exploiting 3D Scene Graphs

2025-08-08

AI Summary

To address the low efficiency and poor interpretability of path planning in indoor 3D scenes, this paper proposes S-Path: a context-aware hierarchical planning method based on a metric-semantic coupled 3D scene graph. The core innovation lies in constructing a semantic graph to enable high-level, interpretable path search, while decomposing low-level planning into parallelizable subproblems. Furthermore, S-Path introduces a subproblem information reuse mechanism and a dynamically updated semantic heuristic function, significantly improving re-planning efficiency. Experimental evaluations in both real-world and simulated indoor environments demonstrate that S-Path reduces average planning time by 5.7× compared to baseline methods, achieves path quality comparable to state-of-the-art sampling-based planners, and exhibits superior robustness in complex indoor scenarios with occlusions, narrow passages, and dynamic obstacles.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Heuristic Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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 5.7x 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.

Problem

• Enhancing path planning efficiency using 3D Scene Graphs
• Improving interpretability through semantic graph-based planning
• Enabling parallel problem-solving via region decomposition

Innovation

• Leverages 3D Scene Graphs for path planning
• Two-stage semantic and parallel subproblem solving
• Replanning reuses solved subproblems for efficiency