DL-SLAM：基于双层次概率的动态环境中高保真高斯泼溅SLAM
DL-SLAM: Enabling High-Fidelity Gaussian Splatting SLAM in Dynamic Environments based on Dual-Level Probability
AI总结：
提出DL-SLAM，通过双层次概率框架结合语义与几何信息，实现动态环境中高保真高斯泼溅SLAM，提升跟踪精度达13%并生成无伪影静态地图。
摘要：
最近在3D高斯泼溅（3DGS）方面的进展使得密集动态同时定位与地图构建（SLAM）取得了显著进展。现有方法通常丢弃预定义的动态物体，忽略了瞬态静态物体为姿态估计提供了有价值的几何约束。最近的一项工作尝试通过使用逐像素不确定性图来量化运动幅度，从而利用这一潜力。虽然这种方法使瞬态静态物体能够增强姿态估计，但它错误地将这些物体整合到静态地图中，导致持续伪影。此外，其对纯几何信息的依赖导致不确定性图中物体边界模糊。为克服这些限制，我们提出了DL-SLAM，一种基于新颖双层次概率框架的单目高斯泼溅SLAM系统。我们的方法通过结合语义和几何信息计算动态概率图。这些像素级概率被提升到3D并聚合，以推导每个实例的物体级动态概率。物体级概率使得能够分类剔除动态高斯体，从而得到无伪影的静态地图。静态地图反过来提供几何一致的指导来细化像素级概率，增强其可靠性。实验结果表明，DL-SLAM优于现有方法，跟踪精度提升高达13%，同时生成高保真语义地图。
英文摘要：
Recent advances in 3D Gaussian Splatting (3DGS) have enabled significant progress in dense dynamic Simultaneous Localization And Mapping (SLAM). Prevailing methods typically discard predefined dynamic objects, ignoring that transiently static objects offer valuable geometric constraints for pose estimation. A recent work attempts to leverage this potential by employing per-pixel uncertainty maps to quantify the magnitude of motion. While this approach enables transiently static objects to enhance pose estimation, it erroneously integrates these objects into the static map, resulting in persistent artifacts. Moreover, its reliance on purely geometric information leads to ambiguous object boundaries in the uncertainty maps. To overcome these limitations, we present DL-SLAM, a monocular Gaussian Splatting SLAM system built upon a novel dual-level probabilistic framework. Our method computes dynamic probability maps by combining semantic and geometric information. These pixel-level probabilities are lifted to 3D and aggregated to derive an object-level dynamic probability for each instance. Object-level probability enables the categorical pruning of dynamic Gaussians, resulting in an artifact-free static map. The static map, in turn, provides a geometrically consistent guidance to refine the pixel-wise probabilities, enhancing their reliability. Experimental results demonstrate that DL-SLAM outperforms existing approaches, improving tracking accuracy by up to 13\% while generating high-fidelity semantic maps.
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