INHerit-SG: 増分階層型セマンティックシーングラフとRAG型検索
INHerit-SG: Incremental Hierarchical Semantic Scene Graphs with RAG-Style Retrieval
ロボットナビゲーション向けに、3D環境を非同期の二重ストリームで階層的なセマンティックシーングラフとして構築し、LLMとトポロジーを組み合わせた検索で複雑な embodied クエリに応答するシステムを提案。
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著者: YukTungSamuel Fang, Zhikang Shi, Jiabin Qiu, Zixuan Chen, Jieqi Shi, Hao Xu, Jing Huo, Yang Gao
分類: cs.RO
原文アブストラクト
Driven by recent advancements in foundation models, semantic scene graphs have emerged as a promising paradigm for high-level 3D environmental abstraction in robot navigation. However, existing frameworks struggle to successfully handle complex embodied queries while ensuring continuous semantic graph construction. To address these limitations, we present INHerit-SG, an asynchronous dual-stream architecture that systematically structures the 3D environment into a RAG-ready knowledge base. Specifically, our framework integrates comprehensive node representations, an event-triggered asynchronous update scheme, and a structured retrieval mechanism. While geometric segmentation is decoupled from semantic reasoning to maintain mapping efficiency, the semantic nodes also store natural language summaries to support text-based retrieval. Furthermore, we propose an interpretable retrieval pipeline that couples the reasoning capabilities of multi-role LLMs with the topological structure of the scene graph, followed by a visual verification process to mitigate false positives. We evaluate INHerit-SG on a newly constructed benchmark for complex embodied semantic query retrieval, HM3DSem-SQR, and in real-world environments. Experiments demonstrate that our system achieves state-of-the-art performance on complex queries, especially for those involving negations and chained spatial constraints. Project Page: https://fangyuktung.github.io/INHeritSG.github.io/
