让机器人通过自然语言在动态环境中实时导航并更新地图
DualMap: Online Open-Vocabulary Semantic Mapping for Natural Language Navigation in Dynamic Changing Scenes
- 采用双地图结构，全局抽象+局部具体，高效处理环境变化
- 无需昂贵3D物体合并，实现快速在线语义建图
- 支持自然语言指令，适合真实场景的机器人导航任务
我们提出DualMap，一种在线开放词汇语义映射系统，使机器人能够通过自然语言查询在动态变化环境中理解与导航。该系统设计用于高效的语义建图并适应环境变化，满足真实世界机器人导航的关键需求。所提出的混合分割前端和对象级状态检测，消除了先前方法中耗时的3D物体合并步骤，实现了高效的在线场景建图。双地图表示结合了全局抽象地图用于高层候选选择，以及局部具体地图用于精确目标到达，有效管理并更新环境中的动态变化。在仿真与真实场景中进行的大量实验表明，DualMap在3D开放词汇分割、高效场景建图和在线语言引导导航方面均达到领先性能。
原文摘要 · Abstract (English)
We introduce DualMap, an online open-vocabulary mapping system that enables robots to understand and navigate dynamically changing environments through natural language queries. Designed for efficient semantic mapping and adaptability to changing environments, DualMap meets the essential requirements for real-world robot navigation applications. Our proposed hybrid segmentation frontend and object-level status check eliminate the costly 3D object merging required by prior methods, enabling efficient online scene mapping. The dual-map representation combines a global abstract map for high-level candidate selection with a local concrete map for precise goal-reaching, effectively managing and updating dynamic changes in the environment. Through extensive experiments in both simulation and real-world scenarios, we demonstrate state-of-the-art performance in 3D open-vocabulary segmentation, efficient scene mapping, and online language-guided navigation. Project page: https://eku127.github.io/DualMap/
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