INHerit-SG：增量式层次语义场景图与RAG风格检索
INHerit-SG: Incremental Hierarchical Semantic Scene Graphs for Complex Embodied Retrieval
AI总结：
本文提出INHerit-SG，通过异步双流架构构建RAG-ready知识库，解决复杂嵌入查询与持续语义图构建问题，实验表明在复杂查询中表现优异。
摘要：
受基础模型进展推动，语义场景图成为机器人导航中高阶3D环境抽象的有前景范式。然而现有框架难以有效处理复杂嵌入查询并保证持续语义图构建。为解决这些限制，我们提出了INHerit-SG，一种异步双流架构，系统性地将3D环境构建成RAG-ready知识库。具体而言，我们的框架整合了全面的节点表示、事件触发的异步更新方案和结构化检索机制。尽管几何分割与语义推理解耦以保持映射效率，语义节点也存储自然语言摘要以支持文本检索。此外，我们提出了一种可解释的检索流程，将多角色LLM的推理能力与场景图的拓扑结构结合，随后通过视觉验证过程以减少假阳性。我们评估了INHerit-SG在一个新构建的复杂嵌入语义查询检索基准HM3DSem-SQR以及现实环境中的表现。实验表明，我们的系统在复杂查询中实现了最先进的性能，尤其在涉及否定和链式空间约束的查询中表现突出。
英文摘要：
Object retrieval in 3D scenes remains difficult when queries combine hierarchical context, attributes, negation, and chained spatial relations. Existing semantic maps, scene graphs, and agentic memory support open-vocabulary retrieval, but jointly resolving these constraints remains challenging. In this work, we propose INHerit-SG, an incremental hierarchical semantic scene graph with an accompanying retrieval method for complex embodied retrieval. Asynchronous geometric and semantic processing and structural event-driven maintenance organize observations into a hierarchy whose node semantics and spatial relations are recorded as natural language text. The same memory supports direct reading and reasoning by more capable language models or query parsing followed by structured retrieval. Controlled comparisons show that direct reading is more accurate with powerful hosted models, while structured retrieval improves the accuracy of smaller local models and lowers per-query API cost for hosted models. We also construct HM3DSem-SQR, containing 6,084 queries from 36 HM3D-Sem scenes, and evaluate on Main-864, a class-balanced subset spanning all 36 scenes, and in three real-world environments. Experiments show that INHerit-SG outperforms the strongest baselines in both simulator and real-world evaluations. The largest gains come on attribute and compositional queries (7.6 and 6.3 strict Top-1 points on Main-864), with a 9.8-point overall success margin on real robots. Project Page: https://fangyuktung.github.io/INHeritSG.github.io/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
