论文/arXiv 2610.07569·22 天前

OpenSplatGraph：从密集语义地图到用于开放词汇机器人感知的结构化场景图

OpenSplatGraph从在线Gaussian开放词汇语义地图构建持久3D场景图，并在标准基准和真实机器人实验中验证。

AI 整理

• 感知与导航
• 评测与安全

原文链接：OpenSplatGraph: From Dense Semantic Maps to Structured Scene Graphs for Open-Vocabulary Robot Perception（arXiv 2610.07569 · 发布日期：2026-10-06）

OpenSplatGraph: From Dense Semantic Maps to Structured Scene Graphs for Open-Vocabulary Robot Perception

OpenSplatGraph从在线Gaussian开放词汇语义地图构建持久3D场景图，并在标准基准和真实机器人实验中验证。

核心要点

• 现有Gaussian Splatting语义地图多为非结构化特征场，限制以对象为中心的推理。
• 方法引入可靠性感知语义场，支持置信度感知、查询条件下的对象提取。
• 提取的对象实例关联到持久图节点，可随观测和查询增量更新属性与关系。
• 评估显示其在在线开放词汇感知和下游机器人任务中取得有竞争力表现，并提供项目页面。

验证范围与局限：验证范围包括标准3D场景理解基准和真实世界机器人实验。

英文摘要 (Abstract)

Dense 3D mapping with semantic understanding is essential for robotic perception in complex environments. Recent 3D Gaussian Splatting-based mapping approaches enable high-fidelity geometry and efficient open-vocabulary perception, but typically represent semantics as unstructured feature fields that limit object-centric reasoning. In contrast, 3D scene graphs explicitly model objects and their relationships for structured reasoning, but are commonly constructed from sparse geometric representations that do not fully exploit dense semantic maps. In this work, we present OpenSplatGraph, a unified framework that constructs persistent 3D scene graphs directly from an online Gaussian-based open-vocabulary semantic map. The proposed framework augments the dense semantic map with a reliability-aware semantic field that maintains lightweight observation statistics for confidence-aware, query-conditioned object extraction. Extracted object instances are associated with persistent graph nodes, allowing object attributes and relationships to be incrementally updated across observations and queries. By tightly coupling dense semantic mapping with persistent object-centric representations, our framework supports both language-guided object grounding and structured relational reasoning while preserving the geometric fidelity of Gaussian-based mapping. Comprehensive evaluations on standard 3D scene understanding benchmarks and real-world robotic experiments demonstrate that OpenSplatGraph achieves competitive performance for online open-vocabulary perception and downstream robotic tasks. Project page: https://csiro-robotics.github.io/OpenSplatGraph.

作者：Binh Long Nguyen, Kien Nguyen, Clinton Fookes, Peyman Moghadam

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2610.07569：OpenSplatGraph: From Dense Semantic Maps to Structured Scene Graphs for Open-Vocabulary Robot Perception访问 2026-10-21