Robust 3DGS-based SLAM via Adaptive Kernel Smoothing

In this paper, we challenge the conventional notion in 3DGS-SLAM that rendering quality is the primary determinant of trackingaccuracy. We argue that, compared to solely pursuing a perfect scenerepresentation, it is more critical to enhance the robustness of the ras-terization process against parameter errors to ensure stable camera posetracking. To address this challenge, we propose a novel approach thatleverages a smooth kernel strategy to enhance the robustness of 3DGS-based SLAM. Unlike conventional methods that focus solely on minimiz-ing rendering error, our core insight is to make the rasterization processmore resilient to imperfections in the 3DGS parameters. We hypothesizethat by allowing each Gaussian to influence a smoother, wider distribu-tion of pixels during rendering, we can mitigate the detrimental effectsof parameter noise from outlier Gaussians. This approach intentionallyintroduces a controlled blur to the rendered image, which acts as a reg-ularization term, stabilizing the subsequent pose optimization. While acomplete redesign of the rasterization pipeline is an ideal solution, wepropose a practical and effective alternative that is readily integratedinto existing 3DGS frameworks. Our method, termed Corrective BlurryKNN (CB-KNN), adaptively modifies the RGB values and locations ofthe K-nearest neighboring Gaussians within a local region. This dynamicadjustment generates a smoother local rendering, reducing the impactof erroneous GS parameters on the overall image. Experimental resultsdemonstrate that our approach, while maintaining the overall quality ofthe scene reconstruction (mapping), significantly improves the robust-ness and accuracy of camera pose tracking.https://github.com/xju-zsh/Robust-3DGS-based-SLAM-via-Adaptive-Kernel-Smoothing.git