用概率方法提升3D高斯点云的定位与建图稳定性
VBGS-SLAM: Variational Bayesian Gaussian Splatting Simultaneous Localization and Mapping
- 将位姿估计与场景优化统一为概率生成模型，显式建模不确定性
- 在长序列追踪中显著减少漂移，真实场景下定位误差降低37%
- 适合需要高鲁棒性、长期运行的机器人导航与自动驾驶应用
3D高斯点云（3DGS）通过高斯混合模型实现高质量3D场景重建，但现有SLAM方法依赖确定性位姿优化，对初始化敏感且易受地图演化影响导致灾难性遗忘。本文提出变分贝叶斯高斯点云SLAM（VBGS-SLAM），将点云优化与相机位姿跟踪融合为生成式概率框架。利用多元高斯共轭性质与变分推断，实现高效闭式更新，并显式维护位姿与场景参数的后验不确定性。该方法有效抑制漂移，提升复杂场景下的鲁棒性，同时保持原有3DGS的高效渲染质量。实验表明，在多种合成与真实场景中，其长序列追踪性能更优，位姿误差降低37%，并支持高效高质量的新视角合成。
原文摘要 · Abstract (English)
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. By leveraging conjugate properties of multivariate Gaussians and variational inference, our method admits efficient closed-form updates and explicitly maintains posterior uncertainty over both poses and scene parameters. This uncertainty-aware method mitigates drift and enhances robustness in challenging conditions, while preserving the efficiency and rendering quality of existing 3DGS. Our experiments demonstrate superior tracking performance and robustness in long sequence prediction, alongside efficient, high-quality novel view synthesis across diverse synthetic and real-world scenes.
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