外观
论文arXiv 2610.2362212 天前
用于移动操作的基础模型驱动3D场景图
原文链接：Foundation-Model-Driven 3D Scene Graphs for Mobile Manipulation（arXiv 2610.23622 · 发布日期：2026-10-16）
Foundation-Model-Driven 3D Scene Graphs for Mobile Manipulation
将FOUND-IT集成到移动操作平台，以基础模型驱动3D场景图作为空间记忆，并在实机实验中验证多种移动操作能力。
核心要点
- 现有基础模型3D场景图虽可实时开放集建图，但融入完整自主流程的系统研究有限。
- 方法将FOUND-IT接入移动操作平台，构建由单目未标定相机生成的任务驱动3D空间记忆。
- 实验展示其适合支持物体变化检测、多次建图会话、可导航路径判断和场景重排抓取。
- 作者基于严格实机实验分析该类3D场景图系统的有效性与局限，并开源代码。
验证范围与局限：验证范围为移动操作平台上的实机实验，任务包括变化检测、建图会话、导航路径判断与场景重排抓取。
英文摘要 (Abstract)
Geometric and Semantic Foundation models are becoming increasingly popular tools for generating spatial maps for robots. Recent work (e.g., FOUND-IT) has leveraged these tools to construct real-time, task-driven, and open-set 3D scene graphs from a single uncalibrated camera. However, while classical robot mapping techniques have undergone extensive system demonstrations, limited work has been done to develop both the fundamental and the systems wide advancements needed to integrate these new types of robot spatial memory into a full, useable autonomy pipeline. In this paper, we bridge this gap, and in doing so we also evaluate the effectiveness and limitations of this type of 3D memory. Towards this end, we integrate FOUND-IT into a mobile manipulator platform and demonstrate it is well-suited to support multiple objectives such as object change detection, support for multiple mapping sessions, determination of navigable paths, and directly support object grasping for the task of scene re-arrangement. Furthermore, we provide nuanced insights into the effectiveness and limitations of a monocular, foundation-model-driven 3D scene graph construction system using observations from rigorous on-robot experiments. Code is available at https://github. com/MIT-SPARK/FOUND-IT.
作者：Dominic Maggio, Lorenzo Shaikewitz, Aryan Naveen, Kris K. Hauser, Luca Carlone
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