OpenMonoGS-SLAM: 单目高斯点云SLAM与开放语义结合
OpenMonoGS-SLAM: Monocular Gaussian Splatting SLAM with Open-set Semantics
AI总结：
OpenMonoGS-SLAM通过结合3DGS与开放集语义，实现无需深度输入的单目SLAM，提升开放世界环境下的感知与建图性能。
摘要：
同时定位与建图（SLAM）是机器人、AR/VR和自主系统中的基础组件。近年来，随着空间AI的日益关注，将SLAM与语义理解结合变得越来越重要，以实现智能感知和交互。最近的努力探索了这种整合，但通常依赖于深度传感器或封闭集语义模型，限制了其在开放世界环境中的可扩展性和适应性。在本工作中，我们提出了OpenMonoGS-SLAM，这是首个将3D高斯点云（3DGS）与开放集语义理解统一的单目SLAM框架。为实现我们的目标，我们利用了最近在视觉基础模型（VFMs）方面的进展，包括MASt3R用于视觉几何和SAM和CLIP用于开放词汇语义。这些模型在多样化任务中提供了稳健的泛化能力，使单目相机跟踪和建图准确，以及在开放世界环境中丰富理解语义。我们的方法不依赖任何深度输入或3D语义地面真实，仅依靠自监督学习目标。此外，我们提出了一种专门设计用于管理高维语义特征的内存机制，有效构建高斯语义特征图，导致整体性能强劲。实验结果表明，我们的方法在封闭集和开放集分割任务中实现了与现有基线相当或更优的性能，且无需依赖深度图或语义注释等辅助传感器。
英文摘要：
Simultaneous Localization and Mapping (SLAM) is a foundational component in robotics, AR/VR, and autonomous systems. With the rising focus on spatial AI in recent years, combining SLAM with semantic understanding has become increasingly important for enabling intelligent perception and interaction. Recent efforts have explored this integration, but they often rely on depth sensors or closed-set semantic models, limiting their scalability and adaptability in open-world environments. In this work, we present OpenMonoGS-SLAM, the first monocular SLAM framework that unifies 3D Gaussian Splatting (3DGS) with open-set semantic understanding. To achieve our goal, we leverage recent advances in Visual Foundation Models (VFMs), including MASt3R for visual geometry and SAM and CLIP for open-vocabulary semantics. These models provide robust generalization across diverse tasks, enabling accurate monocular camera tracking and mapping, as well as a rich understanding of semantics in open-world environments. Our method operates without any depth input or 3D semantic ground truth, relying solely on self-supervised learning objectives. Furthermore, we propose a memory mechanism specifically designed to manage high-dimensional semantic features, which effectively constructs Gaussian semantic feature maps, leading to strong overall performance. Experimental results demonstrate that our approach achieves performance comparable to or surpassing existing baselines in both closed-set and open-set segmentation tasks, all without relying on supplementary sensors such as depth maps or semantic annotations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
