这篇论文搞了个CrossMaps，让漫游车能用自然语言查地图，比VLMaps多了置信度融合和双记忆，在Jetson Orin上实时跑
CrossMaps是一个实时开放词汇语义地图构建管道，使用RGB-D数据生成语言可查询的语义热图。它集成多尺度CLIP嵌入和置信度感知融合，通过几何、语义和时间置信度线索聚合噪声视觉观察。采用双记忆架构（短时记忆STM和长时记忆LTM），置信且一致的单元被提升为持久语义地标。在Jetson Orin驱动UGV上结合SLAM运行，支持自然语言查询导航。
CrossMaps: Confidence-Aware Open-Vocabulary Semantic Mapping for Rover Navigation
Rovers rely on perception to maintain spatial maps that encode both objects and sensor quality (e.g., range reliability, lighting artifacts, data density), guiding data fusion, embedding updates, and navigation under partial observability. To study these coupled perception-navigation processes, we present CrossMaps, a real-time confidence-aware open-vocabulary semantic mapping pipeline that constructs language-queryable maps from RGB-D data. Building on VLMaps-style approaches, CrossMaps integrates multi-scale CLIP embeddings with confidence-aware fusion and a dual-memory architecture consisting of Short-Term Memory (STM) and Long-Term Memory (LTM). The STM aggregates noisy visual observations using geometric, semantic, and temporal confidence cues, while confident and coherent cells are promoted to the LTM as persistent semantic landmarks. Designed for deployment with a Jetson Orin-powered UGV alongside SLAM, CrossMaps runs in real time and produces semantic heatmaps that can be queried with natural language to guide rover navigation.