変化に頑健なオンライン空間意味トポロジカルマッピング
Change-Robust Online Spatial-Semantic Topological Mapping
環境変化に頑健な空間意味推論のため、RGB-Dキーフレームのオンライン・ポーズ認識トポロジカルグラフを提案し、連続SE(3)での逐次仮説検定により曖昧さを扱う。実ロボット実験でSLAMベース手法より頑健性が向上。
詳しい要約
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著者: Jiaming Wang, Jizhuo Chen, Diwen Liu, Atharva Ghotavadekar, Jiaxuan Da, Linh Kästner, Harold Soh
分類: cs.RO
原文アブストラクト
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.
