CrossMaps: 用于漫游车导航的置信度感知开放词汇语义地图
CrossMaps: Confidence-Aware Open-Vocabulary Semantic Mapping for Rover Navigation
AI总结：
提出CrossMaps，一种实时置信度感知开放词汇语义地图构建流水线，通过多尺度CLIP嵌入、置信度融合和双记忆架构生成可查询语义地图，用于漫游车导航。
摘要：
漫游车依赖感知来维护空间地图，该地图编码物体和传感器质量（例如，距离可靠性、光照伪影、数据密度），指导数据融合、嵌入更新以及在部分可观测性下的导航。为了研究这些耦合的感知-导航过程，我们提出了CrossMaps，一种实时的置信度感知开放词汇语义地图构建流水线，该流水线从RGB-D数据构建可语言查询的地图。基于VLMaps风格的方法，CrossMaps集成了多尺度CLIP嵌入、置信度感知融合以及由短期记忆（STM）和长期记忆（LTM）组成的双记忆架构。STM使用几何、语义和时间置信度线索聚合噪声视觉观测，而置信且一致的单元被提升到LTM作为持久语义地标。CrossMaps设计用于与Jetson Orin驱动的UGV以及SLAM一起部署，实时运行并生成语义热力图，可通过自然语言查询来引导漫游车导航。
英文摘要：
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.
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。
