KRVF让边缘机器人用语义体素实时构建可查询的世界模型
KRVF: A Source-Aware Semantic Voxel World Representation for Edge Mobile Manipulation
- 用带来源标识的体素编码占用、颜色、语义证据和时间新鲜度
- 在深度传感器失效时仍能推理物体，避免误修正几何结构
- 支持任务级查询，适合移动机械臂的实时感知与操作
移动操作机器人需要当前、可查询、语义明确且适配边缘计算约束的世界模型。本技术报告提出KRVF，一种面向边缘移动操作的源感知语义体素世界表示。不同于以全局几何保真度为核心的重建导向映射流程，KRVF将局部世界状态表示为任务导向的体素，编码占据、颜色、语义证据、时间新鲜度及证据来源。该表示分离测量占据与语义先验假设，实现深度失败感知的物体推理，避免无声污染持久几何。KRVF还通过渲染地图先验深度实现映射与感知间的反馈闭环，并提供任务级查询算子，用于语义物体与抓取候选。报告形式化定义了KRVF表示，并记录了基于ROS 2的实现，将在线RGB-D观测转化为任务导向的机器人记忆。
原文摘要 · Abstract (English)
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.
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