Traditional Simultaneous Localization and Mapping (SLAM) systems often face limitations including coarse rendering quality, insufficient recovery of scene details, and poor robustness in dynamic environments. 3D Gaussian Splatting (3DGS), with its efficient explicit representation and high-quality rendering capabilities, offers a new reconstruction paradigm for SLAM. This survey comprehensively reviews key technical approaches for integrating 3DGS with SLAM. We analyze performance optimization of representative methods across four critical dimensions: rendering quality, tracking accuracy, reconstruction speed, and memory consumption, delving into their design principles and breakthroughs. Furthermore, we examine methods for enhancing the robustness of 3DGS-SLAM in complex environments such as motion blur and dynamic environments. Finally, we discuss future challenges and development trends in this area. This survey aims to provide a technical reference for researchers and foster the development of next-generation SLAM systems characterized by high fidelity, efficiency, and robustness.

翻译：传统的同步定位与建图（SLAM）系统常面临渲染质量粗糙、场景细节恢复不足以及在动态环境中鲁棒性差等局限。3D高斯溅射（3DGS）凭借其高效的显式表示与高质量渲染能力，为SLAM提供了新的重建范式。本综述全面梳理了将3DGS与SLAM相结合的关键技术路径。我们从渲染质量、跟踪精度、重建速度与内存消耗四个关键维度，剖析了代表性方法的性能优化策略，深入探讨其设计原理与突破点。此外，我们考察了在运动模糊、动态环境等复杂场景中提升3DGS-SLAM鲁棒性的方法。最后，我们讨论了该领域未来面临的挑战与发展趋势。本综述旨在为研究者提供技术参考，推动以高保真、高效率与高鲁棒性为特征的新一代SLAM系统发展。