Towards Next-Generation SLAM: A Survey on 3DGS-SLAM Focusing on Performance, Robustness, and Future Directions

TL;DR

This survey reviews the integration of 3D Gaussian Splatting with SLAM, highlighting advancements in rendering, accuracy, speed, robustness, and outlining future research directions for next-generation SLAM systems.

Contribution

It provides a comprehensive analysis of technical approaches for 3DGS-SLAM, focusing on performance optimization, robustness improvements, and future challenges in the field.

Findings

Enhanced rendering quality and reconstruction speed in 3DGS-SLAM

Improved robustness in dynamic and complex environments

Analysis of key design principles and breakthroughs

Abstract

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.