World SLAM Model：用于SLAM与导航的联合世界建模
World SLAM Model: Joint World Modeling for SLAM and Navigation
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中文总结 AI 辅助
提出World SLAM Model（WSM），将SLAM核心机制融入导航，通过联合训练实现端到端状态维护与几何估计，提升导航性能并保持SLAM精度。
中文摘要 AI 辅助
我们提出了World SLAM Model（WSM），这是一个将SLAM范式直接引入下游导航的统一框架。WSM并未将SLAM仅仅视为提供位姿、地图或令牌的上游模块，而是采用了其核心机制，包括具有持久记忆的增量状态更新以及累积误差的后端细化，从而在交互过程中维持一致的世界状态。给定当前观测和导航目标，WSM预测未来的视觉状态，并联合估计其相机运动与稠密几何，将视觉预测锚定在不断演化的空间世界状态中。该空间状态随新观测的到来而持续更新，并为动作生成和闭环导航提供基础。WSM通过联合导航-SLAM目标进行端到端训练，使下游导航能够直接从SLAM式的状态维护与细化中受益，同时保持精确的几何估计。实验表明，WSM在提升导航性能的同时保持了强大的SLAM精度，凸显了SLAM作为长时程世界建模与具身交互内在机制的潜力。
英文摘要
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.
