A Survey on Collaborative SLAM with 3D Gaussian Splatting

TL;DR

This survey reviews multi-robot collaborative SLAM using 3D Gaussian Splatting, highlighting its advantages, challenges, and future research directions in real-time, high-fidelity scene mapping.

Contribution

It systematically categorizes existing approaches, analyzes core components, and discusses open challenges and future directions in 3D Gaussian Splatting-based collaborative SLAM.

Findings

3D Gaussian Splatting enables real-time high-fidelity rendering for robotics.

Challenges include maintaining global consistency and data fusion in multi-robot systems.

Future research should address lifelong mapping, semantic integration, and bridging the Sim2Real gap.

Abstract

This survey comprehensively reviews the evolving field of multi-robot collaborative Simultaneous Localization and Mapping (SLAM) using 3D Gaussian Splatting (3DGS). As an explicit scene representation, 3DGS has enabled unprecedented real-time, high-fidelity rendering, ideal for robotics. However, its use in multi-robot systems introduces significant challenges in maintaining global consistency, managing communication, and fusing data from heterogeneous sources. We systematically categorize approaches by their architecture -- centralized, distributed -- and analyze core components like multi-agent consistency and alignment, communication-efficient, Gaussian representation, semantic distillation, fusion and pose optimization, and real-time scalability. In addition, a summary of critical datasets and evaluation metrics is provided to contextualize performance. Finally, we identify key open challenges and chart future research directions, including lifelong mapping, semantic association and mapping, multi-model for robustness, and bridging the Sim2Real gap.