Beyond Implicit Representations: Exploring Gaussian Splatting for Next-Generation SLAM, Introduction and Review

Simultaneous localization and mapping (SLAM) has attracted tremendous interest from members of the research community in recent years because of its ability to make the robot truly independent in navigation. The integration of neural rendering and the SLAM system showed promising results in joint localization and photorealistic view reconstruction. However, currently available methods that fully rely on implicit representations are resource hungry. Recent work has shown that 3D Gaussians enable high-quality reconstruction and real-time rendering of scenes using multiple posed cameras. Owing to the increasing popularity of Gaussian splatting (GS) and its expanding research area, we present a comprehensive survey of GS based SLAM papers. This survey addresses the challenges of neural based systems by exploring the application of GS within SLAM frameworks. We present a comprehensive analysis of Gaussian Splatting for scene representation in SLAM, focusing on the past three years of research. Our survey delves into the advantages of GS based SLAM, including efficiency, unified representation for various tasks, suitability for resource-constrained devices, and high accuracy and robustness for dense scene reconstruction. Additionally, the survey includes a broader range of research, including semantic GS techniques, to provide a holistic understanding of this approach. By offering an in-depth exploration of Gaussian Splatting in SLAM, a benchmark comparison of performance, and a focus on 3D Gaussian Splatting (3DGS) based SLAM advantages, this survey aims to be a valuable resource for researchers and developers seeking to leverage Gaussian Splatting for efficient and robust SLAM.