外观
论文arXiv 2608.104492 个月前
PBD-AG：具有不确定性感知检查的持久基线-增量主动图，用于长时程服务机器人
原文链接：PBD-AG: Persistent Baseline-Delta Active Graphs with Uncertainty-Aware Inspection for Long-Horizon Service Robots（arXiv 2608.10449 · 发布日期：2026-08-11）
PBD-AG: Persistent Baseline-Delta Active Graphs with Uncertainty-Aware Inspection for Long-Horizon Service Robots
PBD-AG通过持久基线-增量主动图建模可变物体，在多环境仿真和物理机器人演示中验证长时程感知。
核心要点
- 面向长时程服务机器人，现有在线建图、静态表示或整体视觉语言预测难以可靠处理物体变化。
- 方法将机器人验证的稳定设施与可修订的动态物体事件解耦，并维护可靠性加权的物体状态。
- 检查视点由图条件策略选择，权衡目标覆盖、行驶成本、碰撞风险与冗余观测。
- 多环境仿真显示粗粒度设施F1更高、身份连续性和事件召回更强，并提供项目页面。
验证范围与局限：验证包括多环境仿真与受控动态评估，以及定性物理机器人演示。
英文摘要 (Abstract)
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/
作者：Shuo Bao, Wei Dong, Shuyue Zhang, Ming Shang, Yuchen Huang, Han Yu, Chengjie Xu, Yiheng Bi, Kai Sun, Fuchun Sun
来源
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