AI RESEARCH
VBGS-SLAM: Variational Bayesian Gaussian Splatting Simultaneous Localization and Mapping
arXiv CS.CV
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ArXi:2604.02696v1 Announce Type: new 3D Gaussian Splatting (3DGS) has shown promising results for 3D scene modeling using mixtures of Gaussians, yet its existing simultaneous localization and mapping (SLAM) variants typically rely on direct, deterministic pose optimization against the splat map, making them sensitive to initialization and susceptible to catastrophic forgetting as map evolves. We propose Variational Bayesian Gaussian Splatting SLAM (VBGS-SLAM), a novel framework that couples the splat map refinement and camera pose tracking in a generative probabilistic form.