World SLAM Model：SLAMとナビゲーションのための統合世界モデリング
World SLAM Model: Joint World Modeling for SLAM and Navigation
SLAMの逐次状態更新とバックエンド補正の仕組みをナビゲーションに直接組み込み、将来の視覚状態予測とカメラ運動・密な幾何推定を統合した世界モデルを提案。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Minghui Qin, Yijun Yuan, Weicheng Zheng, Kenan Li, Weibang Wang, Chang Sun, Junhao Huang, Anmin Liu, Yicheng Yao, Hang Zhao
分類: cs.RO, cs.CV
原文アブストラクト
We introduce World SLAM Model (WSM), a unified framework that brings the SLAM paradigm directly into downstream navigation. Rather than treating SLAM merely as an upstream module that provides poses, maps or tokens, WSM adopts its core mechanisms, including incremental state updates with persistent memory and backend refinement of accumulated errors, to maintain a consistent world state during interaction. Given the current observation and a navigation goal, WSM predicts future visual states and jointly estimates their camera motion and dense geometry, grounding visual prediction in an evolving spatial world state. This spatial state is continuously updated as new observations arrive and provides the basis for action generation and closed-loop navigation. WSM is trained end-to-end with a joint navigation--SLAM objective, enabling downstream navigation to benefit directly from SLAM-style state maintenance and refinement while preserving accurate geometric estimation. Experiments demonstrate improved navigation performance together with strong SLAM accuracy, highlighting the potential of SLAM as an intrinsic mechanism for long-horizon world modeling and embodied interaction.
