arXiv subjects

Seungjun Tak

Publications and source records attributed to Seungjun Tak.

3 recordsLinked to original sources

arXiv2026

Real-Time LiDAR Gaussian Splatting SLAM

We present a real-time LiDAR-based framework for Gaussian Splatting SLAM that tightly couples fast G-ICP registration with spherical rasterization-based dense mapping for large-scale sequences. Leveraging LiDAR geometry rather than appearance, we reuse tracking-estimated local covariances to initialize Gaussians with range-aware scales and to derive surface normals for geometry-aware map optimization. We further introduce a covariance-derived geometry score that measures local complexity and drives pruning in planar regions and selective densification in structurally rich areas, while optimized Gaussians and LiDAR-specific confidence cues are fed back to improve tracking robustness. On the Newer College dataset, our method achieves an F-score of 86.78\% using purely online trajectories at real-time speed ($>$20 FPS), and additional experiments on other datasets confirm its stability and scalability.

cs.CV↗

arXiv2026

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

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.

cs.RO↗

arXiv2025

LEGO-SLAM: Language-Embedded Gaussian Optimization SLAM

Recent advances in 3D Gaussian Splatting (3DGS) have enabled Simultaneous Localization and Mapping (SLAM) systems to build photorealistic maps. However, these maps lack the open-vocabulary semantic understanding required for robotic interaction. Integrating language features into SLAM remains a significant challenge, as storing high-dimensional features incurs excessive memory and rendering overhead, while existing methods with static models lack adaptability for novel environments. We propose LEGO-SLAM (Language-Embedded Gaussian Optimization SLAM), a framework that achieves real-time, open-vocabulary mapping within a 3DGS-based SLAM system. At the core of our method is a scene-adaptive autoencoder that distills high-dimensional language embeddings into a compact 16-dimensional feature space, reducing the memory per Gaussian and accelerating rendering. Unlike static approaches, our encoder adapts online to unseen scenes. These compact features also enable a language-guided pruning strategy that identifies semantic redundancy, reducing the map's Gaussian count by up to 58% while maintaining rendering quality. Furthermore, we introduce a language-based loop detection approach that reuses the language features already extracted for mapping, eliminating the need for a separate detection model. Experiments demonstrate that LEGO-SLAM achieves competitive mapping quality and tracking accuracy, all while providing open-vocabulary capabilities at 15 FPS. Our project page is available at https://lab-of-ai-and-robotics.github.io/LEGO-SLAM/

cs.CV↗