通过锚点引导减少视觉与几何漂移，提升长时程导航预测准确性。
Drift-Resistant Navigation World Model with Anchored Epipolar Guidance
- 用稀疏锚点作为长期目标，分段生成中间帧以减少误差累积。
- 在四个基准上显著提升视觉质量、几何一致性与多视角连贯性。
- 适合需要高精度长期预测的机器人导航与规划任务。
我们提出一种抗漂移导航世界模型，有效缓解传统基于滚动预测的世界模型中存在的感知漂移与几何漂移问题。现有方法逐帧递推生成内容，导致噪声累积与预测退化（感知漂移）；同时预测结果常偏离智能体真实运动轨迹，造成几何漂移。为此，我们重构世界模型预测为锚点引导的滚动机制：先预测稀疏的未来锚点作为稳定远距离目标，再在每段内条件化生成中间帧，同时利用过去上下文和未来锚点进行控制。关键的是，这些稀疏锚点结合双向对极几何提供空间约束，指导对应内容在中间帧中的位置。在四个基准上的实验表明，该方法在长时程视觉质量、几何一致性及多视角连贯性方面均优于强基线。性能提升进一步转化为相同规划器下的下游规划效果改进，凸显了抗漂移、几何感知预测对可靠导航世界模型的重要性。
原文摘要 · Abstract (English)
We propose Drift-Resistant Navigation World Model, a generative model that mitigates both perceptual drift and geometric drift in conventional rollout-based navigation world models. Existing methods recursively feed generated content into subsequent steps, causing noise accumulation and degraded predictions, i.e., perceptual drift. Meanwhile, their predictions often deviate from the agent's motion, resulting in geometry drift. We address both types of drift by redesigning world-model prediction as an anchor-guided rollout. Instead of rolling out every frame sequentially, we first predict sparse future anchors that serve as stable long-range targets, and then generate intermediate frames within each chunk conditioned on both past context and future anchors. Importantly, these sparse anchors also provide geometric constraints, supported by bidirectional epipolar geometry, to localize where corresponding content should appear in the intermediate frames. Experiments on four benchmarks demonstrate consistent improvements over strong baselines in long-horizon visual quality, geometric consistency, and multi-view coherence. These gains further translate into improved downstream planning performance under the same planners, highlighting the importance of drift-resistant, geometry-aware prediction for reliable navigation world models.
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