一次构建，持续监控：通过自主探索和开放词汇对象更新的持久语义地图
Build Once, Monitor Continuously: Persistent Semantic Mapping via Autonomous Exploration and Open-Vocabulary Object Updates
AI总结：
提出两阶段系统，先通过前沿探索构建几何地图，再通过开放词汇检测和分割模型更新语义对象图，实现仅重复轻量语义阶段的高效持久监控。
摘要：
对仓库、医院和办公室等室内空间的持久语义监控要求机器人反复监控环境并跟踪物体随时间的变化。当环境几何结构保持不变而只有物体移动时，每次访问都从头运行完整的同步定位与地图构建（SLAM）及密集语义重建是冗余的。我们提出一个模块化的两阶段系统，将几何建图与语义更新分离。第一阶段，采用带有动态搜索窗口的基于前沿的探索方法构建2D占用网格。第二阶段，机器人在此地图中重新定位，并使用开放词汇物体检测器和可提示分割模型构建语义对象图。后续访问仅重复轻量语义阶段，因此系统能很好地适应频繁重访。对象图使用基于类别和距离的关联规则来更新对象，使地图能反映会话内变化（单次遍历中的对象变化）和会话间变化（重访之间的变化），例如物体被移动、移除或添加。我们在两个实际室内环境（约8500平方米和117平方米）中使用Fetch机器人验证系统，并报告多次更新迭代的精确率、召回率和F1分数。
英文摘要：
Persistent semantic monitoring of indoor spaces such as warehouses, hospitals, and offices requires a robot to repeatedly monitor an environment and track how objects change over time. Running full simultaneous localization and mapping (SLAM) with dense semantic reconstruction from scratch on every visit is redundant when the environment geometry stays the same and only the objects move. We present a modular two-stage system that separates geometric mapping from semantic updating. In the first stage, a frontier-based exploration method with a dynamic search window builds a 2D occupancy grid. In the second stage, the robot relocalizes in this map and builds a semantic object graph using an open-vocabulary object detector and a promptable segmentation model. Only the lightweight semantic stage is repeated on later visits, so the system scales well to frequent revisits. The object graph uses a category and distance based association rule to update objects, which lets the map reflect both intra-session changes (object changes within a single traversal) and inter-session changes (changes across revisits), such as objects being moved, removed, or added. We validate the system on a Fetch robot in two real indoor environments of about 8,500 sq.m and 117 sq.m, and report precision, recall, and F1 scores across multiple update iterations.
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。
