Spectral GS-SLAM：面向实时3D高斯泼溅SLAM的可观测性感知与退化鲁棒跟踪
Spectral GS-SLAM: Observability-Aware, Degeneracy-Robust Tracking for Real-Time 3D Gaussian Splatting SLAM
AI总结：
提出Spectral GS-SLAM，通过自适应补偿退化场景中的欠约束方向，结合ICP与特征约束实现鲁棒跟踪，并利用高斯感知平面性加权机制融合几何信息，在无结构/纹理环境中保持实时性能（40.14 FPS）。
摘要：
最近的3DGS-SLAM系统通过利用传统的特征匹配或基于ICP的跟踪实现实时操作，从而避免了早期方法中使用的密集光度优化。然而，特征匹配在无纹理环境中容易失败，而基于ICP的跟踪在无结构或几何退化场景中由于病态优化而难以应对。为了解决这个问题，我们提出了Spectral GS-SLAM，一个高效且鲁棒的跟踪框架，将ICP与互补的特征约束相结合。我们的方法通过自适应补偿退化场景中的欠约束方向来缓解数值不稳定性，而不干扰用于建图的共享高斯表示。我们进一步引入了一种高斯感知平面性加权机制，利用3D高斯的固有协方差结构来表征场景几何并指导信息融合。在具有挑战性的TUM RGB-D序列上的广泛评估表明，Spectral GS-SLAM实现了实时性能（40.14 FPS），同时在无结构和无纹理环境中保持一致的跟踪。所提出的方法在退化场景中保持轨迹完整性，同时在非恶劣条件下保持竞争性能。
英文摘要：
Recent 3DGS-SLAM systems enable real-time operation by leveraging conventional feature matching or ICP-based tracking, thereby avoiding the heavy dense photometric optimization used in earlier approaches. However, feature matching remains prone to failure in textureless environments, while ICP-based tracking struggles in structureless or geometrically degenerate scenes due to ill-conditioned optimization. To address this issue, we propose Spectral GS-SLAM, an efficient yet robust tracking framework that integrates ICP with complementary feature-based constraints. Our method mitigates numerical instability by adaptively compensating under-constrained directions in degenerate scenarios, without interfering with the shared Gaussian representation used for mapping. We further introduce a Gaussian-aware planarity weighting mechanism that exploits the intrinsic covariance structure of 3D Gaussians to characterize scene geometry and guide information fusion. Extensive evaluations on challenging TUM RGB-D sequences demonstrate that Spectral GS-SLAM achieves real-time performance (40.14 FPS) while maintaining consistent tracking in both structureless and featureless environments. The proposed method preserves trajectory integrity in degenerate scenes while maintaining competitive performance in non-adverse conditions.
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