PRISM-SLAM: Probabilistic Ray-Grounded Inference for Scale-aware Metric SLAM

TL;DR

PRISM-SLAM is a real-time monocular SLAM system that integrates vision foundation model priors into a Bayesian framework to achieve scale-aware, metric-consistent localization and mapping in dynamic environments.

Contribution

It introduces a novel Plücker Ray-Distance Factor and a Dynamic Scene Uncertainty Gating mechanism to address scale ambiguity and environmental dynamics.

Findings

Achieves nearly oracle-aligned $SE(3)$ ATE on benchmarks.

Provides verified metric trajectories without post-hoc scale correction.

Operates at 30 FPS using only RGB input.

Abstract

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 Pl\"ucker 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/