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

TL;DR

This paper introduces LangGS-SLAM, a real-time RGB-D SLAM system that reconstructs language-aligned dense feature fields, achieving high geometric fidelity and efficient semantic mapping at 15 FPS, bridging 3D perception and language reasoning.

Contribution

It presents a novel real-time SLAM system with a Top-K Rendering pipeline and hybrid optimization for dense language-aligned features, improving geometric fidelity and efficiency.

Findings

Achieves superior geometric fidelity over geometric-only baselines.

Maintains comparable semantic fidelity to offline methods.

Operates at 15 frames per second with dense feature fields.

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.