3D高斯溅射让机器人地图更清晰、更稳定，是下一代SLAM的关键。
Towards Next-Generation SLAM: A Survey on 3DGS-SLAM Focusing on Performance, Robustness, and Future Directions
- 用3D高斯溅射替代传统表示，提升重建质量与渲染效果
- 在动态场景中保持定位精度，抗模糊能力更强
- 适合研究高效鲁棒的下一代三维定位与建图系统
传统同时定位与地图构建（SLAM）系统常面临渲染质量粗糙、场景细节恢复不足以及动态环境下鲁棒性差的问题。3D高斯溅射（3DGS）凭借其高效的显式表示和高质量渲染能力，为SLAM提供了新的重建范式。本文全面综述了3DGS与SLAM融合的关键技术路径，从渲染质量、跟踪精度、重建速度和内存消耗四个关键维度分析代表性方法的设计原理与突破。进一步探讨了在运动模糊和动态环境等复杂条件下提升3DGS-SLAM鲁棒性的方法。最后，讨论该领域未来挑战与发展趋势。本综述旨在为研究人员提供技术参考，推动高保真、高效、鲁棒的下一代SLAM系统发展。
原文摘要 · Abstract (English)
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.
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