PRISM-SLAM: 面向尺度感知度量SLAM的概率射线基础推理
PRISM-SLAM: Probabilistic Ray-Grounded Inference for Scale-aware Metric SLAM
AI总结：
提出PRISM-SLAM框架，通过将视觉基础模型先验集成到贝叶斯因子图中，利用Plücker射线距离因子和动态场景不确定性门控机制，实现无尺度漂移的实时单目度量SLAM。
摘要：
单目SLAM历来在动态环境中存在尺度模糊和跟踪失败的问题。虽然最近的视觉基础模型（VFM）提供了显著的零样本深度先验，但简单地整合这些确定性预测忽略了预测不确定性和帧间尺度不一致性。我们提出了PRISM-SLAM，一个实时框架，将VFM先验严格集成到结构化的贝叶斯因子图中，以实现尺度感知、度量一致的定位与建图。具体来说，我们引入了Plücker射线距离因子，将单目观测锚定在全局一致的度量坐标系中的绝对空间，通过使度量尺度Fisher可识别，从数学上解决了尺度漂移。为了处理环境动态，我们从时间深度一致性中推导出认知不确定性代理，并设计了动态场景不确定性门控（DSUG）机制。这种软门控方法概率性地降低动态干扰物的权重，而不会产生与传统语义分割掩码相关的高计算开销。通过采用多进程架构异步处理VFM推理和几何跟踪，PRISM-SLAM仅使用RGB输入即可在30 FPS下提供验证的度量输出，弥合了基础模型与现实机器人应用之间的差距。在TUM RGB-D和7-Scenes基准上的评估表明，PRISM-SLAM的度量$SE(3)$绝对轨迹误差（ATE）几乎与其对齐的$Sim(3)$误差相同。这表明我们的系统能够生成可直接部署的度量轨迹，无需任何后处理尺度校正。项目页面：https://prismslam-cmd.github.io/prismslam_pr/
英文摘要：
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/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
