提出CROSS框架，让机器人在环境变化时仍能稳定定位与导航。
Change-Robust Online Spatial-Semantic Topological Mapping
- 用带位姿的拓扑图替代传统度量地图，提升对环境变化的鲁棒性。
- 通过连续假设检验处理感知模糊，在光照变化和家具重组下仍保持定位准确。
- 适合动态场景下的自主机器人导航，尤其适用于光照突变或物体移动的场景。
自主机器人需要具备应对环境变化的空间语义推理能力：利用空间与语义知识判断去向、路径及自身位置。现有方法通常将语义附加于SLAM构建的度量地图，但在外观变化和场景动态下，数据关联与重定位性能下降。本文提出一种面向变化鲁棒的在线空间-语义拓扑表示（CROSS），以在线、位姿感知的RGB-D关键帧拓扑图替代全局一致的度量基础。系统在连续SE(3)空间中通过序列假设检验显式处理感知模糊性，其估计器维护一个有界高斯混合信念，实现对回环闭合与被劫持机器人事件的合理处理。在严重外观变化条件下的实验，包括真实机器人在光照变化与家具重排下的物体目标导航任务，验证了该方法相比基于SLAM与拓扑基线显著提升鲁棒性，且在感知伪影下仍能保证安全运行。
原文摘要 · Abstract (English)
Autonomous robots require change-robust spatial-semantic reasoning: using spatial and semantic knowledge to decide where to go, how to get there, and where the robot is despite environmental change. Existing approaches typically attach semantics to SLAM-built metric maps, but these pipelines are brittle under appearance shifts and scene dynamics, where data association and relocalization degrade. We propose a Change-Robust Online Spatial-Semantic (CROSS) representation that replaces a globally consistent metric substrate with an online, pose-aware topological graph of RGB-D keyframes. The system explicitly reasons over perceptual ambiguity using sequential hypothesis testing in continuous SE(3). Our estimator maintains a bounded Gaussian-mixture belief over poses, enabling principled handling of loop closures and kidnapped-robot events. Experiments under severe appearance change, including real-robot object-goal navigation with lighting shifts and furniture rearrangement, demonstrate improved robustness over SLAM-based and topological baselines while remaining safe under perceptual aliasing.
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