MoGe2-SLAM: Outdoor Monocular 3D Gaussian Splatting SLAM with Absolute Scale

Abstract

3D Gaussian Splatting (3DGS) has significantly advanced the field of visual SLAM. However, outdoor monocular SLAM systems still suffer from scale ambiguity, so recovering the absolute scale remains difficult, and tracking often becomes brittle in large scenes. To address these issues, we present MoGe2-SLAM, an outdoor monocular 3DGS-SLAM framework that incrementally recovers camera trajectories and dense maps in an approximate absolute metric scale from a monocular RGB sequence with a known camera-height prior. By approximate absolute metric scale, we mean that camera poses and the reconstructed map are expressed in meters after camera-height calibration, rather than only up to an unknown global scale. Our core idea is to anchor frozen MoGe-2 geometry to a physical camera-height/ground-plane ruler, enabling approximate absolute-scale reconstruction. Specifically, we perform Absolute-Scale Initialization on the first keyframe by replacing MoGe-2’s advertised metric global scale with a known camera height and ground-plane geometry, generating the initial Gaussian scene directly in physical units. We then adopt Grid-Balanced Tracking that encourages spatially distributed feature extraction and maintains stable tracking. For mapping, we employ an ORB-anchored strategy that aligns the MoGe-2 depth prior with the rendered depth using geometric constraints, thereby mitigating scale drift. On KITTI, Waymo, and Argoverse 2, MoGe2-SLAM is competitive with existing monocular SLAM baselines in trajectory shape and novel-view fidelity while recovering trajectories and Gaussian maps in an approximate physical scale under the stated camera-height prior.