SCOUT: 基于不确定性引导遍历的语义场景覆盖
SCOUT: Semantic scene COverage via Uncertainty-guided Traversal
AI总结：
提出SCOUT框架，通过不确定性引导的遍历规划与概率场景图构建的闭环，使机器人主动探索并逐步理解环境，实现语义场景完整性作为操作目标。
摘要：
长时间运行的机器人不应仅仅访问空间，而应逐步理解空间。然而，大多数3D场景图管线将感知视为固定数据集上的后处理阶段，将场景表示与决定首先观察什么的决策解耦。我们提出SCOUT，一种在线语义探索框架，通过将主动遍历与概率场景图构建耦合来闭合这一循环。给定先验2D占用地图和带姿态的RGB-D观测，SCOUT增量构建一个不确定性感知的3D场景图，其节点维护融合的几何和开放词汇对象标签的后验信念，而边编码结构关系，如在上、内部、属于和旁边。这些信念被反馈给不确定性引导的遍历规划器，该规划器通过平衡期望语义确定性增益、几何覆盖增益和旅行成本来选择视点。这样，当额外证据重要时，机器人重新访问模糊对象，当场景不完整时，扩展到未见的自由空间。由此产生的系统将语义场景完整性视为操作目标，而非语义映射的被动副产品，朝着能够在最少人工干预下巡逻、更新和推理不断变化的室内环境的自主智能体迈进。
英文摘要：
Robots that operate over extended periods should not merely visit space; they should progressively understand it. Yet most 3D scene graph pipelines treat perception as a post-processing stage over a fixed dataset, decoupling scene representation from the decisions that determine what is observed in the first place. We present SCOUT, an online semantic exploration framework that closes this loop by coupling active traversal with probabilistic scene graph construction. Given a prior 2D occupancy map and posed RGB-D observations, SCOUT incrementally builds an uncertainty-aware 3D scene graph whose nodes maintain fused geometry and posterior beliefs over open-vocabulary object labels, while edges encode structural relations such as on, inside, belong, and next to. These beliefs are fed back to an uncertainty-guided traversal planner, which selects viewpoints by balancing expected semantic certainty gain, geometric coverage gain, and travel cost. In this way, the robot revisits ambiguous objects when additional evidence matters and expands into unseen free space when the scene remains incomplete. The resulting system treats semantic scene completeness as an operational objective rather than a passive by-product of semantic mapping, moving toward autonomous agents that can patrol, update, and reason about evolving indoor environments with minimal human intervention.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
