PanoGS-SLAM: Panoramic 3D Gaussian Splatting SLAM

• URL: http://arxiv.org/abs/2609.17387v1
• Date: Tue, 15 Sep 2026 16:26:12 GMT
• Title: PanoGS-SLAM: Panoramic 3D Gaussian Splatting SLAM
• Abstract summary: We present PanoGS-SLAM, the first panoramic dense SLAM system built on 3D Gaussian Splatting.<n>Our method per- forms differentiable rendering and pose optimization directly in the spherical domain.<n>Experiments show that PanoGS-SLAM consistently outperforms geometric and GS-based baselines in tracking accuracy and rendering quality.
• Score: 20.25032556921585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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