From the 1 of 8 linked papers with an AI index.
8 papers
LEGO-SLAM: Language-Embedded Gaussian Optimization SLAM
Sibaek Lee, Seongbo Ha, Kyeongsu Kang +3
LEGO-SLAM integrates compressed language embeddings into a 3D Gaussian Splatting SLAM system, enabling real-time open‑vocabulary mapping, semantic pruning, and loop detection.
Real-Time LiDAR Gaussian Splatting SLAM
Seungjun Tak, Yewon Jeon, Jaeik Hwang +3
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-sca…
TriLift: Interpolation-Free Tri-Plane Lifting for Efficient 3D Perception on Embedded Systems
Sibaek Lee, Jiung Yeon, Hyeonwoo Yu
Dense 3D convolutions provide high accuracy for perception but are too computationally expensive for real-time robotic systems. Existing tri-plane methods rely on 2D image features…
LAMP: Implicit Language Map for Robot Navigation
Sibaek Lee, Hyeonwoo Yu, Giseop Kim +1
Recent advances in vision-language models have made zero-shot navigation feasible, enabling robots to follow natural language instructions without requiring labeling. However, exis…
GSO-SLAM: Bidirectionally Coupled Gaussian Splatting and Direct Visual Odometry
Jiung Yeon, Seongbo Ha, Hyeonwoo Yu
We propose GSO-SLAM, a real-time monocular dense SLAM system that leverages Gaussian scene representation. Unlike existing methods that couple tracking and mapping with a unified s…
OpenMonoGS-SLAM: Monocular Gaussian Splatting SLAM with Open-set Semantics
Jisang Yoo, Gyeongjin Kang, Hyun-kyu Ko +2
Simultaneous Localization and Mapping (SLAM) is a foundational component in robotics, AR/VR, and autonomous systems. With the rising focus on spatial AI in recent years, combining…