发表机构
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对3DGS SLAM中协方差同时用于渲染与配准的冲突，提出双协方差参数化，分离渲染与跟踪协方差，并利用跟踪协方差锚定角点，在多个数据集上实现鲁棒实时跟踪并降低漂移。
AI 中文摘要
基于ICP的3D高斯溅射（3DGS）SLAM通过将传入帧与地图高斯进行配准来实现实时跟踪，每个图元的协方差同时用于渲染和配准。这两种用途对同一协方差提出了相互冲突的要求。建图器通过最小化光度误差来塑造协方差，通常使其沿表面扁平化，而鲁棒配准通常受益于测量不确定性。我们提出了一种双协方差参数化方法。每个高斯保持单一均值，但拥有两个协方差：一个由建图器优化的渲染协方差，以及一个由RGB-D传感器噪声模型推导的跟踪协方差。我们进一步将跟踪协方差用作图像角点的高斯锚点，在深度几何较弱的方向上提供约束。我们在TUM RGB-D、ScanNet、Replica以及使用RealSense D435i在轮式和手持平台上记录的两个室外序列上进行了评估。我们在多个场景中实现了鲁棒的跟踪性能，并减少了里程计漂移，同时以约60 FPS的速度进行跟踪。
英文摘要
ICP-based 3D Gaussian Splatting (3DGS) SLAM tracks in real time by registering incoming frames against map Gaussians, using each primitive's covariance for both rendering and registration. These two uses place conflicting demands on one covariance. The mapper shapes it to minimize photometric error, often flattening it against surfaces, while robust registration typically benefits from measurement uncertainty. We propose a dual-covariance parameterization. Each Gaussian keeps a single mean but holds two covariances: a rendering covariance optimized by the mapper, and a tracking covariance derived from an RGB-D sensor noise model. We further use the tracking covariances as Gaussian anchors for image corners, providing constraints in directions where depth geometry is weak. We evaluate on TUM RGB-D, ScanNet, Replica, and two outdoor sequences recorded with a RealSense D435i on wheeled and handheld platforms. We achieve robust tracking performance across multiple scenes and reduced odometry drift, while tracking at $\sim$ 60 FPS.
