# MyGO-Splat: Multi-Objective Closed-Loop Geometric Feedback for RGB-Only Gaussian SLAM

arXiv: https://arxiv.org/abs/2606.29738 · HTML: https://mlsanity.com/paper/2606.29738/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2026.

## Built on by (most cited first, 2 of 2)

- [SSMB: Self-Supervised Local Feature Detection under Motion Blur](https://mlsanity.com/paper/2608.27181/) (2026)
- [F2 SLAM: Turning Feed-Forward Geometry into Persistent Factors for SLAM](https://mlsanity.com/paper/2610.05207/) (2026)

## Abstract (arXiv)

Real-time monocular Simultaneous Localization and Mapping (SLAM) fundamentally suffers from scale ambiguity and a lack of geometric self-correction. While 3D Gaussian Splatting (3DGS) enables high-fidelity rendering, existing RGB-only systems remain open-loop because depth priors are injected into mapping but refined geometry cannot effectively regulate tracking drift. We present MyGO-Splat, a closed-loop Gaussian SLAM framework that analytically rasterizes Gaussian primitives into pixel-wise depth and surface normals, allowing the map to actively supervise camera pose optimization. To bridge monocular priors and scale consistency, our framework introduces scale-aware adaptive alignment that projects foundation-model depth estimates into the globally optimized Gaussian space, forming a self-correcting cycle for scale feedback. Extensive evaluations show that this closed-loop design improves scale stability and appearance-geometry consistency, achieving performance comparable to RGB-D methods while using only monocular input.