融合定位与动作条件预测，实现长期一致性建模。
J-LAW: Joint Localization and Action-Conditioned World Modeling via Coupled Latent Factor Graphs
- 用联合因子图统一表示位置、预测状态和持久特征点
- 在部分观测下提升长时预测一致性，减少漂移
- 适合需要精准规划的机器人系统开发
传统同时定位与地图构建（SLAM）仅估计度量位姿和几何地图，无法提供动作条件的预测状态。动作条件世界模型虽能学习紧凑的隐状态动态，却忽略全局度量一致性，且在开环推演中产生累积漂移。本文提出J-LAW（联合定位与动作条件世界建模），一种统一的因子图框架，将度量位姿变量、预测隐状态与持续存在的隐特征点关联起来。J-LAW将每张图像表示为紧凑的预测状态，并通过独立学习的映射将其与位姿或运动测量结合。其最大后验（MAP）因子图在时间上强制这些互补信息源的一致性。在PushT和WildGS数据集上的实验表明，J-LAW的因子图表示可提升长时隐状态一致性，并在部分观测下恢复更可靠的预测状态，为未来的集成定位与规划系统奠定基础。
原文摘要 · Abstract (English)
Classical simultaneous localization and mapping (SLAM) estimates metric poses and a geometric map but does not provide an action-conditioned predictive state. Action-conditioned world models learn compact latent dynamics but ignore global metric consistency and accumulate drift under open-loop rollout. We introduce J-LAW (Joint Localization and Action-Conditioned World Modeling), a unified factor-graph formulation that connects metric pose variables, predictive latent states, and persistent latent landmarks in this letter.J-LAW represents each image as a compact predictive state and combines it with pose or motion measurements through a separately learned mapping. Its maximum a posteriori (MAP) factor graph enforces consistency between these complementary sources of information over time. Experiments on PushT and WildGS show that J-LAW's factor-graph representation can improve long-horizon latent consistency and recover more reliable predictive states under partial observations, forming a foundation for future integrated localization and planning systems.
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