DualMap: 用于动态变化场景中自然语言导航的在线开放词汇语义映射
DualMap: Online Open-Vocabulary Semantic Mapping for Natural Language Navigation in Dynamic Changing Scenes
AI总结：
DualMap通过自然语言查询实现动态环境中的高效语义映射与导航，采用混合分割前端和对象级状态检查提升效率，展现先进性能。
摘要：
我们介绍了DualMap，一种在线开放词汇映射系统，使机器人能够通过自然语言查询理解并导航动态变化的环境。该系统旨在实现高效的语义映射和对变化环境的适应性，满足真实世界机器人导航应用的基本要求。我们提出的混合分割前端和对象级状态检查消除了先前方法所需的昂贵3D对象合并，实现了高效的在线场景映射。双地图表示结合了一个用于高层候选选择的全局抽象地图和一个用于精确目标到达的局部具体地图，有效管理并更新环境中的动态变化。通过在仿真和现实场景中的广泛实验，我们展示了在3D开放词汇分割、高效场景映射和在线语言引导导航方面的最先进性能。项目页面：https://eku127.github.io/DualMap/
英文摘要：
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/
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。
