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Compact Keyframe-Optimized Multi-Agent Gaussian Splatting SLAM

Monica M. Q. Li, Pierre-Yves Lajoie, Jialiang Liu, Giovanni Beltrame

Abstract

Efficient multi-agent 3D mapping is essential for robotic teams operating in unknown environments, but dense representations hinder real-time exchange over constrained communication links. In multi-agent Simultaneous Localization and Mapping (SLAM), systems typically rely on a centralized server to merge and optimize the local maps produced by individual agents. However, sharing these large map representations, particularly those generated by recent methods such as Gaussian Splatting, becomes a bottleneck in real-world scenarios with limited bandwidth. We present an improved multi-agent RGB-D Gaussian Splatting SLAM framework that reduces communication load while preserving map fidelity. First, we incorporate a compaction step into our SLAM system to remove redundant 3D Gaussians, without degrading the rendering quality. Second, our approach performs centralized loop closure computation without initial guess, operating in two modes: a pure rendered-depth mode that requires no data beyond the 3D Gaussians, and a camera-depth mode that includes lightweight depth images for improved registration accuracy and additional Gaussian pruning. Evaluation on both synthetic and real-world datasets shows up to 85-95\% reduction in transmitted data compared to state-of-the-art approaches in both modes, bringing 3D Gaussian multi-agent SLAM closer to practical deployment in real-world scenarios. Code: https://github.com/lemonci/coko-slam

Compact Keyframe-Optimized Multi-Agent Gaussian Splatting SLAM

Abstract

Efficient multi-agent 3D mapping is essential for robotic teams operating in unknown environments, but dense representations hinder real-time exchange over constrained communication links. In multi-agent Simultaneous Localization and Mapping (SLAM), systems typically rely on a centralized server to merge and optimize the local maps produced by individual agents. However, sharing these large map representations, particularly those generated by recent methods such as Gaussian Splatting, becomes a bottleneck in real-world scenarios with limited bandwidth. We present an improved multi-agent RGB-D Gaussian Splatting SLAM framework that reduces communication load while preserving map fidelity. First, we incorporate a compaction step into our SLAM system to remove redundant 3D Gaussians, without degrading the rendering quality. Second, our approach performs centralized loop closure computation without initial guess, operating in two modes: a pure rendered-depth mode that requires no data beyond the 3D Gaussians, and a camera-depth mode that includes lightweight depth images for improved registration accuracy and additional Gaussian pruning. Evaluation on both synthetic and real-world datasets shows up to 85-95\% reduction in transmitted data compared to state-of-the-art approaches in both modes, bringing 3D Gaussian multi-agent SLAM closer to practical deployment in real-world scenarios. Code: https://github.com/lemonci/coko-slam

Paper Structure

This paper contains 14 sections, 3 equations, 2 figures, 5 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Multi-Agent SLAM
  4. Keyframing
  5. 3D Gaussian compaction
  6. Loop closure for 3DGS SLAM
  7. Method
  8. Keyframing using Feature Vector
  9. 3D Gaussian Compaction
  10. Loop closure between 3D Gaussian submaps
  11. Evaluation
  12. Results
  13. Ablation Study
  14. Conclusion

Figures (2)

  • Figure 1: Top view comparison of merged maps of two agents in scene office-0 of the Replica dataset hu2024cg. Our method, Coko-SLAM, merges the submaps from different agents correctly while CP-SLAM hu2023cp (a), MAGiC-SLAM yugay2024magicslammultiagentgaussianglobally (b), and MAC-Ego3D xu2025mac (c) fail without initial relative poses between agents. The maps in (a) and (d) are merged using rendered images from the submaps; the maps in (b), (c), and (e) are merged using depth images directly from the camera.
  • Figure 2: We adapt a keyframing method to select keyframes based on distance in feature space. We then eliminate redundant 3D Gaussians during mapping. The agents then send pure 3D Gaussian submaps with keyframe feature vectors to a server for loop closure and map merging, without the initial relative poses among agents.