TL;DR
Experiments show that MS-SLAM stabilizes per-frame processing while maintaining competitive trajectory accuracy and rendering quality, and further demonstrate the feasibility of online RGB-D mapping in real indoor scenes.
Real-time dense SLAM is a core capability for robotics applications that require robust localization and high- quality mapping in dynamic or fast-changing environments. Recent 3D Gaussian Splatting (3DGS)-based SLAM methods have shown promising performance, but most are designed for narrow-FoV pinhole cameras, where li...
This work proposes LightSplat, a hybrid-representation RGB-D SLAM framework that synergizes local sparse features for robust and fast tracking with a dual-thread backend that progressively constructs dense Gaussian submaps, and achieves the online reconstruction of high-fidelity Gaussian map.
This work presents CGS-SLAM, a hybrid decentralized/centralized system enabling multi-agent 3DGS SLAM using only RGB and inertial data, and demonstrates competitive tracking performance, improved rendering quality over state-of-the-art methods, and accurate submap alignment.
This paper proposes LV-GS SLAM, a novel system that integrates LiDAR and visual data for incremental, large-scale reconstruction with real-time tracking, and develops a keyframe-based submap management framework that dynamically adjusts memory allocation based on both primitive density and inter-frame overlap ratio, ef...
3D Gaussian Splatting has emerged as a promising map representation that allows autonomous robots to localize themselves while reconstructing a photorealistic environment. However, its robustness in real-world deployments remains limited: the lack of effective noise mitigation and efficient loop closure leads to the ac...
G3M-SLAM is proposed, an anchor-guided Gaussian memory framework for UAV-oriented dense SLAM with representation-level submap fusion with recovery from split-agent degradation and compact representation exchange rather than an improvement over full-sequence single-agent processing.
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