AI 中文总结
该研究提出SSTG-Nav，将一次性勘测转化为可执行物体目标，在HM3D-v2数据集实验中显著提升语义导航的成功率与SPL，验证了预探索在可靠重复语义导航中的实用性。
AI 中文摘要
在家庭、办公室和设施中连续运行数月的服务机器人应凭借经验变得更可靠，而非每次都从头搜索熟悉空间以响应请求。然而，物体导航（ObjectNav）目前主要被设定为单次探索任务，留下了一个核心部署挑战：识别出物体并不意味着找到了可停靠的可达位置，且一次置信度高的地图错误就可能导致任务终止。我们提出SSTG-Nav，这是一种可重复的度量语义记忆，可将一次性勘测转化为可执行的物体目标，整合不同视角的证据，并保留空间上不同的恢复停靠点。在36个场景的1000个HM3D-v2 episodes中，我们的与目标无关的拓扑达到了99.4%的几何成功上限。在保持语义响应固定的情况下，度量 grounding 将SR/SPL从0.835/0.560提升至0.920/0.603，而源感知融合达到0.926/0.586。感知融合的Top-3恢复将Success@1/2/3提升至0.928/0.965/0.975，并达到0.601的SPL@3。通过模型、视野、密度和损坏控制可确定这些提升的来源，ROS2/Nav2实现展示了完整的可重复查询到执行的流程。综合来看，这些结果确立了预探索作为可靠、可重复语义导航的强大实用范式。
英文摘要
Service robots operating for months in the same homes, offices, and facilities should become more reliable with experience instead of searching familiar space from scratch for every request. Yet ObjectNav is predominantly formulated as one-shot exploration, leaving a central deployment challenge unresolved: recognizing an object does not identify a reachable place to stop, and one confident map error can terminate the task. We introduce SSTG-Nav, a reusable metric-semantic memory that turns a one-time survey into actionable object goals, consolidates evidence across viewpoints, and retains spatially distinct recovery standoffs. On 1,000 HM3D-v2 episodes across 36 scenes, our goal-independent topology achieves a 99.4% geometric success ceiling. Holding semantic responses fixed, metric grounding raises SR/SPL from 0.835/0.560 to 0.920/0.603, and source-aware fusion reaches 0.926/0.586. Fusion-aware Top-3 recovery raises Success@1/2/3 to 0.928/0.965/0.975 and reaches 0.601 SPL@3. Model, field-of-view, density, and corruption controls identify where these gains originate, and a ROS2/Nav2 realization demonstrates the complete reusable query-to-execution pipeline. Together, the results establish pre-exploration as a powerful practical regime for dependable, repeated semantic navigation.
