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This paper introduces PBD-AG, a persistent baseline-delta active graph framework designed for long-horizon service robots to autonomously build and revise world models in dynamic environments. By decoupling stable fixtures from dynamic object events and utilizing a geometric visibility gate, PBD-AG effectively mitigates localization errors and enhances object state reliability. Simulation results demonstrate that PBD-AG outperforms capability-matched controls in terms of aggregate coarse-fixture F1, identity continuity, and event recall, showcasing its potential for robust robotic perception in complex settings.
PBD-AG achieves superior object state reliability and dynamic event recall, setting a new standard for long-horizon robotic perception in unpredictable environments.
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/
