解决动态视觉惯性里程计中提示不稳定导致的误差问题。

Dynamic visual-inertial odometry (VIO) requires reliable suppression of motion-corrupted measurements, yet prior semantic-assisted approaches depend on category-limited segmenters and degrade under partial occlusion. Promptable foundation segmentation models offer category-agnostic dynamic parsing, but their effectiveness in VIO depends critically on the temporal stability of input prompts---a factor largely overlooked in existing pipelines. When prompts derived from raw detection are jittery or intermittent under occlusion, the resulting masks flicker across frames, destabilizing geometric estimation. We propose STAG-VIO, which formulates dynamic robustness as a perception-to-geometry interface stabilization problem. We introduce uncertainty-adaptive multi-object tracking that models prompt generation as state estimation with bounded noise adaptation, producing temporally coherent box prompts. These stabilized prompts drive a lightweight foundation segmenter whose masks undergo geometry-oriented morphological refinement to establish conservative safety margins. A constraint-budget-aware feature redistribution strategy preserves well-conditioned static measurements when dynamic regions dominate the view. Experiments on VIODE and OpenLORIS-Scene show consistent gains over state-of-the-art baselines. Ablation confirms that prompt stabilization is the single most impactful component, reducing trajectory error by up to 83%.