GS3LAM: 基于高斯语义散射的SLAM
GS3LAM: Gaussian Semantic Splatting SLAM
AI总结：
GS3LAM通过多模态数据融合实现实时一致的语义密集地图，采用语义高斯场和多模态误差约束联合优化，引入深度自适应尺度正则化和随机采样关键帧映射策略，提升跟踪鲁棒性和渲染质量。
摘要：
最近，RGB、深度和语义的多模态融合在密集同时定位与建图（SLAM）中展现出巨大潜力。然而，生成一致语义地图的前提是需要密集、高效且可扩展的场景表示。现有的基于显式表示的语义SLAM系统通常受限于分辨率和无法预测未知区域。相反，隐式表示通常依赖于耗时的光线追踪，无法满足实时需求。幸运的是，3D高斯散射（3DGS）作为一种结合点方法效率和几何结构连续性的表示方法，已崭露头角。为此，我们提出了GS3LAM，一种基于高斯语义散射的SLAM框架，通过多模态数据渲染一致的密集语义地图。GS3LAM将场景建模为语义高斯场（SG-Field），并通过多模态误差约束联合优化相机姿态和场。此外，引入了深度自适应尺度正则化（DSR）方案，以解决尺度不变高斯与几何表面之间的对齐问题。为缓解灾难性遗忘，我们提出了基于随机采样的关键帧映射（RSKM）策略，其在常见局部共视优化方法上表现出更优性能。在基准数据集上的大量实验表明，GS3LAM在跟踪鲁棒性、渲染质量和语义精度方面优于现有最先进方法。源代码可在https://github.com/lif314/GS3LAM上获得。
英文摘要：
Recently, the multi-modal fusion of RGB, depth, and semantics has shown great potential in dense Simultaneous Localization and Mapping (SLAM). However, a prerequisite for generating consistent semantic maps is the availability of dense, efficient, and scalable scene representations. Existing semantic SLAM systems based on explicit representations are often limited by resolution and an inability to predict unknown areas. Conversely, implicit representations typically rely on time-consuming ray tracing, failing to meet real-time requirements. Fortunately, 3D Gaussian Splatting (3DGS) has emerged as a promising representation that combines the efficiency of point-based methods with the continuity of geometric structures. To this end, we propose GS3LAM, a Gaussian Semantic Splatting SLAM framework that processes multimodal data to render consistent, dense semantic maps in real-time. GS3LAM models the scene as a Semantic Gaussian Field (SG-Field) and jointly optimizes camera poses and the field via multimodal error constraints. Furthermore, a Depth-adaptive Scale Regularization (DSR) scheme is introduced to resolve misalignments between scale-invariant Gaussians and geometric surfaces. To mitigate catastrophic forgetting, we propose a Random Sampling-based Keyframe Mapping (RSKM) strategy, which demonstrates superior performance over common local covisibility optimization methods. Extensive experiments on benchmark datasets show that GS3LAM achieves increased tracking robustness, superior rendering quality, and enhanced semantic precision compared to state-of-the-art methods. Source code is available at https://github.com/lif314/GS3LAM.
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