机器人可长期追踪室内物体变化，省去重复建图
Build Once, Monitor Continuously: Persistent Semantic Mapping via Autonomous Exploration and Open-Vocabulary Object Updates
- 分两阶段：先建几何地图，再轻量更新语义
- 支持物体移动、增删，多次访问仍保持高精度
- 适合仓库、医院等需持续监控的场景
持久化监测室内环境（如仓库、医院、办公室）需要机器人反复巡查并跟踪物体随时间的变化。当环境几何结构不变而仅物体位置变动时，每次重跑完整的语义SLAM冗余且低效。本文提出一种模块化双阶段系统：第一阶段采用基于前缘探索与动态搜索窗口的方法构建2D占用栅格地图；第二阶段机器人在已有地图中重新定位，并利用开放词汇目标检测器与可提示分割模型构建语义对象图。后续访问仅重复轻量级语义更新阶段，系统可高效支持高频重访。对象图通过类别与距离关联规则实现更新，能反映会话内（单次巡检中）和会话间（多次巡检间）的物体变化，如移动、移除或新增。我们在一台Fetch机器人上于两个真实室内环境（约8,500平方米和117平方米）验证系统，报告了多轮更新迭代下的精确率、召回率和F1分数。
原文摘要 · Abstract (English)
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.
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