同步定位与建图（Simultaneous Localization and Mapping, SLAM）是指在未知环境中同时实现自主移动机器人的定位和环境地图构建的问题，其在机器人技术和自动驾驶等领域有着重要价值。本文首先回顾了SLAM技术的发展历程，从早期的手工特征提取方法到现代的深度学习驱动的解决方案。其中，基于神经辐射场（Neural Radiance Fields, NeRF）的SLAM方法利用神经网络进行场景表征，进一步提高了建图的可视化效果。然而，这类方法在渲染速度上仍然面临挑战，限制了其实时应用的可能性。相比之下，基于高斯溅泼（Gaussian Splatting, GS）的SLAM方法以其实时的渲染速度和照片级的场景渲染效果，为SLAM领域带来了新的研究热点和机遇。接着，文中按照RGB/RGBD、多模态数据以及语义信息三种不同应用类型对基于高斯溅泼的SLAM方法进行了分类和总结，并针对每种情况讨论了相应SLAM方法的优势和局限性。最后，针对当前基于高斯溅泼的SLAM方法面临的实时性、基准一致化、大场景的扩展性以及灾难性遗忘等问题进行分析，并对未来研究方向进行了展望。通过这些探讨和分析，旨在为SLAM领域的研究人员和工程师提供全面的视角和启发，帮助分析和理解当前SLAM系统面临的关键问题，推动该领域的技术进步和应用拓展。

Simultaneous Localization and Mapping (SLAM) has undergone a profound evolution, transitioning through various stages and methodologies, each of which has contributed significantly to advancements in accuracy, robustness, and applicability across diverse scenarios. This paper provides a comprehensive exploration of the historical development and current trends in SLAM, with a particular focus on the progression from manual feature extraction to the adoption of modern deep learning and 3D graphics-based approaches. In the early stages of SLAM development, the process relied heavily on manual feature extraction, where visual features were carefully selected and extracted by human operators to facilitate localization and mapping tasks. While this method proved effective in relatively simple environments, it was highly susceptible to the complexities of more dynamic scenes and variations in illumination. The dependency on human intervention for feature selection not only limited the scalability of these systems but also constrained their robustness in dynamic or unpredictable environments.The introduction of visual SLAM marked a pivotal advancement in the field. By leveraging the rapid progress in computer vision technologies, such as improved feature matching algorithms and visual odometry, visual SLAM systems significantly enhanced both the robustness and accuracy of SLAM. These innovations enabled SLAM systems to perform more reliably across a wide array of real-world environments, thereby paving the way for more automated and efficient approaches to simultaneous localization and mapping. The integration of deep learning into SLAM methodologies represents a paradigm shift in scene understanding and reconstruction. One of the most notable advancements in this area is the emergence of Neural Radiance Fields (NeRF)-based SLAM methods. NeRF-based approaches are capable of modeling dense depth and color information with unprecedented accuracy, providing a more detailed and precise understanding of the environment. However, these methods are not without their challenges, particularly in terms of computational efficiency and real-time performance. Such challenges are especially pertinent in applications where rapid data processing and immediate response are crucial. In response to the limitations of existing SLAM methods, 3D Gaussian Splatting (3DGS) technology has emerged as a promising alternative. 3DGS-based SLAM methods offer significant improvements in rendering speed and high-fidelity scene reconstruction. This technology enhances both the speed and quality of spatial data processing, making it particularly well-suited for applications such as augmented reality and autonomous navigation systems. The inherent robustness of 3DGS-based SLAM methods makes them especially effective in large-scale environments and scenarios characterized by dynamic changes. The categorization of SLAM methodologies can be further refined by considering the types of sensory inputs and the specific application needs they address. Initially, SLAM methods utilizing simple RGB and RGB-D sensors were primarily focused on capturing visual and depth information, which proved effective in controlled environments, particularly indoors. These methods excel in scenarios where there is ample lighting, well-defined textures, and minimal occlusions. However, in more challenging conditions—such as outdoor environments, low-texture or textureless surfaces, and areas with significant lighting variations—RGB and RGB-D SLAM methods often face substantial limitations. For instance, in outdoor environments, changes in lighting, shadows, or exposure to direct sunlight can severely degrade the performance of these methods, leading to inaccuracies in both localization and mapping. Similarly, in textureless regions like white walls or glass surfaces, RGB-D sensors struggle to capture sufficient visual features, resulting in failed or unreliable reconstructions. These limitations underscore the need for more robust SLAM solutions that can operate effectively across a broader range of conditions. Multimodal SLAM approaches have been developed to address these challenges by integrating data from multiple sensors, such as LiDAR, thermal cameras, and inertial measurement units (IMUs). By combining visual data with other sensory inputs, multimodal SLAM systems can overcome the weaknesses inherent in RGB and RGB-D-based methods. For example, LiDAR can provide accurate depth measurements even in low-light or textureless environments, while thermal cameras can detect heat signatures, aiding in environments where visual data is insufficient or unreliable. Additionally, IMUs contribute to maintaining accurate localization in scenarios with rapid motion or poor visual conditions by providing supplementary motion and orientation data. In scenarios where precise geometric reconstruction and higher-level environmental understanding are required, recent advancements have combined semantic information with SLAM methods based on 3D Gaussian Splatting (3DGS) technology. By incorporating semantic cues, these methods enhance not only the accuracy of scene reconstruction and localization but also provide crucial perceptual information for downstream tasks such as robotic navigation, augmented reality, and embodied intelligence. This integration allows SLAM systems to interpret and adapt to complex, dynamic environments more effectively, making them suitable for a wide range of real-world applications, from indoor mapping to outdoor navigation in varied lighting and textural conditions. The nuanced understanding of when to deploy RGB or RGB-D SLAM versus multimodal SLAM is critical for optimizing the performance and applicability of these systems. While RGB and RGB-D methods are efficient and effective in controlled, well-lit indoor environments, multimodal SLAM approaches are indispensable for applications in outdoor, textureless, or dynamically changing environments where robustness and adaptability are paramount. Despite the significant advancements in SLAM technology, current 3DGS-based methods still face several challenges. These include issues related to scalability in large-scale scenes, adaptability to dynamic environments, and the optimization required for real-time performance. Addressing these challenges is a key focus of ongoing research, which aims to integrate deep learning techniques with traditional geometric methods. Such integration is expected to further enhance the overall performance and versatility of SLAM systems. Additionally, the establishment of unified evaluation benchmarks is crucial for standardizing performance metrics across different SLAM methodologies. Such benchmarks would facilitate greater transparency and comparability in research outcomes, thereby driving further innovation in the field. In conclusion, the evolution of SLAM methodologies—from manual feature extraction to deep learning and 3D graphics-based approaches—has significantly advanced the capabilities of simultaneous localization and mapping systems. By examining the historical developments, current methodologies, and future research directions, this paper provides researchers and engineers with comprehensive insights into the complexities and opportunities associated with advancing SLAM technology. Continued innovation and interdisciplinary collaboration will be essential in driving further advancements, enabling SLAM systems to fulfill their potential across a wide range of practical applications.