FOCUS：面向不确定性感知半静态场景的细粒度开放词汇变化检测

中文摘要 AI 辅助

长期运行的自主机器人必须在世界随访问而变化时保持其环境记忆的更新。在半静态环境中，一个物体可能被另一个几何和语义上相似但不同的实例原地替换，使得仅从几何或粗略语义难以检测身份变化。我们提出了FOCUS，一个用于物体级变化检测和地图维护的不确定性感知框架。我们将半静态记忆维护表述为概率推断，融合了几何似然与从3D高斯地图渲染的外观似然。一个递归的三状态估计器维护每个已映射物体是持续存在、被替换还是被移除的状态。为了考虑不完美的3DGS渲染，我们将渲染的外观证据建模为概率形式，而不是直接用作变化分数，模型参数通过初始建图会话的无变化回放自动校准。我们在一个新的Isaac Sim仓库基准上评估了我们的方法，该基准包含模糊的原地替换，并在真实世界的TorWIC数据集上进行了评估。与概率基线相比，它将物体级替换F1分数从0.20提高到0.84，并且无需手动参数调整即可迁移到真实世界数据。

英文摘要

Autonomous robots operating over long periods must keep their environmental memory up to date as the world changes between visits. In semi-static environments, an object may be replaced in place by a different but geometrically and semantically similar instance, making the identity change difficult to detect from geometry or coarse semantics alone. We present FOCUS, an uncertainty-aware framework for object-level change detection and map maintenance. We formulate semi-static memory maintenance as probabilistic inference that fuses geometric likelihood with appearance likelihood rendered from a 3D Gaussian map. A recursive three-state estimator maintains whether each mapped object is PERSISTED, REPLACED, or REMOVED. To account for imperfect 3DGS rendering, we model the rendered appearance evidence probabilistically rather than using it as a direct change score, with the model parameters automatically calibrated from a change-free replay of the initial mapping session. We evaluate our method on a new Isaac Sim warehouse benchmark with ambiguous in-place replacements and on the real-world TorWIC dataset. It improves object-level replacement F1 from 0.20 to 0.84 over a probabilistic baseline and transfers to real-world data without manual parameter retuning.

发表机构

• University of Toronto Institute for Aerospace Studies（多伦多大学航空航天研究所）
• University of Toronto Robotics Institute（多伦多大学机器人研究所）

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