DeltaSeek：面向不断演化的施工环境中的主动感知
DeltaSeek: Toward Active Perception in Evolving Construction Environments
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中文总结 AI 辅助
针对施工环境动态变化导致静态感知失效的问题，提出DeltaSeek框架，形式化具身允许观察集，并通过实验区分感知限制与获取成本，验证腕部相机在解决底盘无法观察的变化上更高效。
中文摘要 AI 辅助
施工环境持续演化，引起巨大的几何变化，这会降低静态建图和配准的性能。这需要主动感知，即机器人有意识地选择感知配置以解析环境的当前状态。我们提出DeltaSeek，一个面向演化建筑环境中主动感知的初步框架。虽然我们的更广泛目标是构建一个能推理在何处、如何以及何时进行观察的系统，但本文解决一个关键先决条件：机器人的感知具身如何约束其能够获取的观察。我们形式化了一个具身的允许观察集，并在一个基于IFC的基准上，使用配备UR5e的Husky A300，在底盘安装和腕部安装的RGB-D配置下进行评估，通过几何可见性对观察进行评分，并通过可驾驶距离对努力进行评分。在一个包含八项受控变化、跨越四种可观察性条件的房间尺度场景中，对240个允许的底盘位姿和五种手臂姿态的穷举评估表明，有两项变化不允许任何底盘视角，而腕部相机解决了这两项。对于两种具身都观察到的变化，腕部相机的中位底盘行驶距离为6.0米，而底盘相机为15.2米。这些结果区分了感知限制与获取成本，阐明了观察是不可能的还是仅仅需要更多的行驶。
英文摘要
Construction environments evolve continuously, causing large geometric changes that degrade static mapping and registration performance. This necessitates active perception, where robots deliberately select sensing configurations to resolve the environment's current state. We present DeltaSeek, an initial framework toward active perception in evolving built environments. While our broader objective is a system that reasons about where, how, and when to observe, this paper addresses a critical prerequisite: how a robot's sensing embodiment constrains the observations it can acquire. We formalize an embodiment's permissible observation set and evaluate with a Husky A300 equipped with a UR5e on an IFC-derived benchmark under chassis-mounted and wrist-mounted RGB-D configurations, scoring observations by geometric visibility and effort by drivable distance. In a room-scale scene with eight controlled changes spanning four observability conditions, exhaustive evaluation over 240 permissible base poses and five arm postures shows that two changes admit no chassis viewpoint whatsoever, while the wrist camera resolves both. For changes observed by both embodiments, the median base travel is $6.0$~m for the wrist camera and $15.2$~m for the chassis camera. These results distinguish sensing limitations from acquisition costs, clarifying whether an observation is impossible or simply requires more travel.
发表机构
- University of Texas at Arlington（德克萨斯大学阿灵顿分校）
机构由 AI 辅助整理，请以论文原文为准。
