发表机构
Zhejiang University; Ant Group; National University of Defense Technology（浙江大学; 蚂蚁集团; 国防科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对窄视场相机SLAM位姿可观测性差的问题，提出首个基于3D高斯泼溅的全景稠密SLAM系统，通过球面域可微渲染和深度引导初始化，在PALVIO和SynPano基准上实现更优的跟踪精度与渲染质量。
AI 中文摘要
实时稠密SLAM是机器人应用的核心能力，这些应用需要在动态或快速变化的环境中实现鲁棒的定位和高质量建图。近年来基于3D高斯泼溅（3DGS）的SLAM方法展现出有前景的性能，但大多数是为窄视场（FoV）针孔相机设计的，其有限的角覆盖范围削弱了位姿可观测性，并且在快速运动和大视角变化下常常导致光度优化不稳定。我们提出了PanoGS-SLAM，这是首个基于3D高斯泼溅的全景稠密SLAM系统。我们的方法直接在球面域中进行可微渲染和位姿优化，从而实现全方位光度约束以获得更稳定的跟踪。为了提高几何一致性和鲁棒性，我们引入了（1）一种球面一致的光度损失，用于补偿等距柱状投影的面积畸变，以及（2）一种深度引导的高斯初始化策略，用于稳定新观察区域中的增量建图。在真实和合成全景基准（PALVIO和SynPano）上的大量实验表明，PanoGS-SLAM在跟踪精度和渲染质量方面持续优于几何基线和基于GS的基线，同时实现了快速的前端收敛和实时性能。此外，受控视场实验揭示了随着角覆盖范围的增加，优化条件和收敛稳定性呈现明显的单调改善，突出了感知几何在塑造基于可微高斯SLAM优化景观中的基本作用。源代码将公开发布。
英文摘要
Real-time dense SLAM is a core capability for robotics applications that require robust localization and high- quality mapping in dynamic or fast-changing environments. Recent 3D Gaussian Splatting (3DGS)-based SLAM methods have shown promising performance, but most are designed for narrow-FoV pinhole cameras, where limited angular coverage weakens pose observability and often leads to unstable photo- metric optimization under rapid motion and large viewpoint changes. We present PanoGS-SLAM, the first panoramic dense SLAM system built on 3D Gaussian Splatting. Our method per- forms differentiable rendering and pose optimization directly in the spherical domain, enabling omnidirectional photometric constraints for more stable tracking. To improve geometric consistency and robustness, we introduce (1) a sphere-consistent photometric loss that compensates for the area distortion of equirectangular projection, and (2) a depth-guided Gaussian initialization strategy that stabilizes incremental mapping in newly observed regions. Extensive experiments on both real and synthetic panoramic benchmarks (PALVIO and SynPano) show that PanoGS-SLAM consistently outperforms geometric and GS-based baselines in tracking accuracy and rendering quality, while achieving fast front-end convergence and real-time perfor- mance. In addition, controlled field-of-view experiments reveal a clear monotonic improvement in optimization conditioning and convergence stability as angular coverage increases, high- lighting the fundamental role of sensing geometry in shaping the optimization landscape of differentiable Gaussian-based SLAM. The source code will be made publicly available.
