KRVF：面向边缘移动操作的源感知语义体素世界表示
KRVF: A Source-Aware Semantic Voxel World Representation for Edge Mobile Manipulation
AI总结：
提出KRVF，一种源感知语义体素表示，用于边缘移动操作，通过任务导向体素编码占用、语义、时间新鲜度和证据源，实现深度故障感知推理和映射-感知闭环。
摘要：
移动操作器需要当前、可查询、有语义意义且能在边缘计算约束下使用的世界模型。本技术报告提出KRVF，一种用于边缘移动操作的源感知语义体素世界表示。与主要优化全局几何保真度的重建中心映射管线不同，KRVF将局部世界状态表示为任务导向体素，编码占用、颜色、语义证据、时间新鲜度和证据源。该表示将测量的占用与语义先验假设分离，实现深度故障感知的对象推理，而不会静默地破坏持久几何。KRVF还通过渲染地图先验深度进行修复，闭合映射与感知之间的反馈循环，并为语义对象和抓取候选提供任务级查询操作符。报告形式化了KRVF表示，并记录了将在线RGB-D观测转化为任务导向机器人记忆的ROS 2实现。
英文摘要：
Mobile manipulators need world models that are current, queryable, semantically meaningful, and usable under edge-compute constraints. This technical report presents KRVF, a source-aware semantic voxel world representation for edge mobile manipulation. Unlike reconstruction-centric mapping pipelines that primarily optimize global geometric fidelity, KRVF represents local world state as task-oriented voxels that encode occupancy, color, semantic evidence, temporal freshness, and evidence source. The representation separates measured occupancy from semantic-prior hypotheses, enabling depth-failure-aware object reasoning without silently corrupting persistent geometry. KRVF also closes a feedback loop between mapping and sensing by rendering map-prior depth for repair, and exposes task-level query operators for semantic objects and grasp candidates. The report formalizes the KRVF representation and documents a ROS 2 implementation that turns online RGB-D observations into a task-facing robot memory.
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。
