PBD-AG：面向长周期服务机器人的具有不确定性感知检查的持久基线-增量主动图
PBD-AG: Persistent Baseline-Delta Active Graphs with Uncertainty-Aware Inspection for Long-Horizon Service Robots
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中文总结 AI 辅助
针对长周期服务机器人世界模型构建的现有方法存在不足，本文提出PBD-AG框架，通过解耦稳定固定装置与动态对象事件，结合不确定性感知检查，在仿真与物理机器人实验中取得更优性能。
中文摘要 AI 辅助
长周期服务机器人需要能够在未知环境中自主构建并随任务相关对象变化而修正的持久世界模型。现有方法依赖在线建图，这类方法会累积定位与观测误差；或采用无法捕捉持久对象变化的静态场景表示；或采用缺乏可验证三维几何证据的整体视觉-语言预测。我们提出PBD-AG，这是一种持久基线-增量主动图框架，该框架将机器人验证的稳定固定装置与可修正的动态对象事件解耦。在我们的框架下，机器人通过机载探索自主引导结构基线，并检查已发现的固定装置以确立分层对象信念。PBD-AG维护几何、语义、身份、存在及支撑关系上的可靠性加权对象状态，利用几何可见性门缓解遮挡下的错误删除。检查视角由图条件策略选择，该策略平衡目标覆盖范围、移动成本、碰撞风险及冗余观测。在多种环境及受控动态评估下的仿真实验显示，与能力匹配的对照组相比，PBD-AG具有更高的聚合粗固定装置F1值，以及更强的身份连续性和事件召回率。定性物理机器人演示进一步说明了其与机载感知的集成，为长周期机器人提供可追踪的世界模型。PBD-AG项目页面可通过此https URL访问。
英文摘要
Long-horizon service robots require persistent world models that can be built autonomously in unseen environments and revised as task-relevant objects change. Existing methods rely on online mapping, which accumulates localization and observation errors, static scene representations that cannot capture persistent object changes, or holistic vision-language predictions that lack verifiable 3D geometric evidence. We present PBD-AG, a persistent baseline-delta active graph framework that decouples robot-verified stable fixtures from revisable dynamic object events. Under our framework, the robot autonomously bootstraps the structural baseline from onboard exploration and inspects discovered fixtures to ground hierarchical object beliefs. PBD-AG maintains reliability-weighted object states over geometry, semantics, identity, existence, and support relations, utilizing a geometric visibility gate to mitigate false deletions under occlusion. Inspection viewpoints are selected by a graph-conditioned policy that balances target coverage, travel cost, collision risk, and redundant observation. Simulation experiments in multiple environments and under controlled dynamic evaluation show higher aggregate coarse-fixture F1 than capability-matched controls, as well as stronger identity continuity and event recall. A qualitative physical-robot demonstration further illustrates integration with onboard sensing, providing a traceable world model for long-horizon robotic perception. The project page of PBD-AG is available at https://shuobao214.github.io/PBD-AG/
