用概率方法融合视觉模型，解决单目定位的尺度模糊问题。
PRISM-SLAM: Probabilistic Ray-Grounded Inference for Scale-aware Metric SLAM
- 构建贝叶斯因子图，用射线距离因子固定绝对尺度
- 动态场景下通过不确定性软抑制干扰，精度接近理想情况
- 30帧/秒实时运行，仅需彩色图像，适合机器人应用
单目SLAM长期面临尺度模糊和动态环境跟踪失败问题。尽管视觉基础模型（VFMs）提供出色的零样本深度先验，但直接使用其确定性预测会忽略预测不确定性与帧间尺度不一致。我们提出PRISM-SLAM，一个实时框架，将VFMs先验严格集成到结构化贝叶斯因子图中，实现感知尺度、度量一致的定位与建图。具体地，引入普吕克射线-距离因子，在全局一致的度量坐标系中锚定单目观测，数学上使尺度可辨识，从而解决尺度漂移。为处理环境动态性，基于时间深度一致性推导认知不确定性代理，并设计动态场景不确定性门控（DSUG）机制。该软门控方法概率性降低动态干扰权重，避免传统语义分割掩码带来的高计算开销。通过异步处理视觉模型推理与几何追踪的多进程架构，PRISM-SLAM仅用RGB输入即实现30 FPS的验证度量输出，弥合了基础模型与真实机器人应用之间的鸿沟。在TUM RGB-D与7-Scenes基准上评估，其度量SE(3)绝对轨迹误差（ATE）几乎与理想对齐的Sim(3)误差相当，证明系统无需后处理尺度校正即可生成可用部署的度量轨迹。
原文摘要 · Abstract (English)
Monocular SLAM historically suffers from scale ambiguity and tracking failure in dynamic environments. While recent vision foundation models (VFMs) provide remarkable zero-shot depth priors, naively integrating these deterministic predictions ignores predictive uncertainty and frame-to-frame scale inconsistencies. We propose PRISM-SLAM, a real-time framework that rigorously integrates VFM priors into a structured Bayesian factor graph to achieve scale-aware, metric-consistent localization and mapping. Specifically, we introduce a Plücker Ray-Distance Factor to anchor monocular observations in absolute space within a globally consistent metric coordinate system, mathematically resolving scale drift by making the metric scale Fisher-identifiable. To handle environmental dynamics, we derive an epistemic uncertainty proxy from temporal depth consistency and formulate a Dynamic Scene Uncertainty Gating (DSUG) mechanism. This soft-gating approach probabilistically down-weights dynamic distractors without incurring the heavy computational overhead associated with traditional semantic segmentation masks. By employing a multi-process architecture that asynchronously processes VFM inference and geometric tracking, PRISM-SLAM provides verified metric output at 30 FPS using solely RGB input, bridging the gap between foundation models and real-world robotic applications. Evaluated on the TUM RGB-D and 7-Scenes benchmarks, PRISM-SLAM achieves a metric $SE(3)$ Absolute Trajectory Error (ATE) nearly identical to its oracle-aligned $Sim(3)$ error. This demonstrates that our system can produce deployment-ready metric trajectories by delivering robust metric SLAM solutions without any post-hoc scale correction. Project page: https://prismslam-cmd.github.io/prismslam_pr/
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