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arXiv · 2608.26868

CGS-SLAM: Collaborative Gaussian Splatting based SLAM for Multi-Agent Reconstruction

Jean-Daniel de Ambrogi·Aladine Chetouani·Vincent Nguyen·Aurélien Chateigner
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Abstract

Recent advances in SLAM have leveraged 3DGS for photorealistic reconstruction and novel view synthesis. However, most methods rely on RGB-D input, which is unavailable on consumer-grade smartphones, and few integrate 3DGS within a collaborative framework. Therefore, we present CGS-SLAM, a hybrid decentralized/centralized system enabling multi-agent 3DGS SLAM using only RGB and inertial data. Each agent performs local tracking with inertial data as a motion prior and reconstructs a scaled map using a metric monocular depth estimator (Depth Pro). Keyframe encodings are shared among agents, enabling dynamic keyframing in regions of spatial overlaps with other agents, enhancing submap alignment. Afterwards, a central server aligns submaps using VGGT as a view alignment model. This bidirectional communication keeps communication cost low during mapping and global reconstruction in difficult GNSS-denied environments. Experiments on multiple datasets demonstrate competitive tracking performance, improved rendering quality over state-of-the-art methods, and accurate submap alignment.

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Jean-Daniel de Ambrogi, Aladine Chetouani, Vincent Nguyen, Aurélien Chateigner. 2026-08-27. CGS-SLAM: Collaborative Gaussian Splatting based SLAM for Multi-Agent Reconstruction. https://arxiv.org/abs/2608.26868

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