2 citations · 2 across the 10 of their papers we have counts for
10 papers
BayesianGS-SLAM: Uncertainty-Aware Neural Rendering SLAM via Probabilistic Formulation
Kyeongsu Kang, Seongbo Ha, Sibaek Lee +1
Neural-rendering-based SLAM relies on rendered RGB-D residuals for camera tracking and map optimization, but the reliability of these predictions can vary substantially because of…
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…
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…
LangGS-SLAM: Real-Time Language-Feature Gaussian Splatting SLAM
Seongbo Ha, Sibaek Lee, Kyungsu Kang +3
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-…
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…
LEGO-SLAM: Language-Embedded Gaussian Optimization SLAM
Sibaek Lee, Seongbo Ha, Kyeongsu Kang +3
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-vo…
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…
Bayesian NeRF: Quantifying Uncertainty with Volume Density for Neural Implicit Fields
Sibeak Lee, Kyeongsu Kang, Seongbo Ha +1
We present a Bayesian Neural Radiance Field (NeRF), which explicitly quantifies uncertainty in the volume density by modeling uncertainty in the occupancy, without the need for add…
RGBD GS-ICP SLAM
Seongbo Ha, Jiung Yeon, Hyeonwoo Yu
Simultaneous Localization and Mapping (SLAM) with dense representation plays a key role in robotics, Virtual Reality (VR), and Augmented Reality (AR) applications. Recent advanceme…