LangGS-SLAM: Real-Time Language-Feature Gaussian Splatting SLAM

• URL: http://arxiv.org/abs/2602.06991v1
• Date: Wed, 28 Jan 2026 05:35:34 GMT
• Title: LangGS-SLAM: Real-Time Language-Feature Gaussian Splatting SLAM
• Authors: Seongbo Ha, Sibaek Lee, Kyungsu Kang, Joonyeol Choi, Seungjun Tak, Hyeonwoo Yu,
• Abstract summary: RGB-D SLAM system reconstructs a language-aligned dense feature field while sustaining low-latency tracking and mapping.<n>System achieves superior geometric fidelity compared to geometric-only baselines and comparable semantic fidelity to offline approaches while operating at 15 FPS.
• Score: 2.738569311610586
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose a RGB-D SLAM system that reconstructs a language-aligned dense feature field while sustaining low-latency tracking and mapping. First, we introduce a Top-K Rendering pipeline, a high-throughput and semantic-distortion-free method for efficiently rendering high-dimensional feature maps. To address the resulting semantic-geometric discrepancy and mitigate the memory consumption, we further design a multi-criteria map management strategy that prunes redundant or inconsistent Gaussians while preserving scene integrity. Finally, a hybrid field optimization framework jointly refines the geometric and semantic fields under real-time constraints by decoupling their optimization frequencies according to field characteristics. The proposed system achieves superior geometric fidelity compared to geometric-only baselines and comparable semantic fidelity to offline approaches while operating at 15 FPS. Our results demonstrate that online SLAM with dense, uncompressed language-aligned feature fields is both feasible and effective, bridging the gap between 3D perception and language-based reasoning.

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