5 papers · 1 filter
G2S-ICP SLAM: Geometry-aware Gaussian Splatting ICP SLAM
Gyuhyeon Pak, Hae Min Cho, Euntai Kim
In this paper, we present a novel geometry-aware RGB-D Gaussian Splatting SLAM system, named G2S-ICP SLAM. The proposed method performs high-fidelity 3D reconstruction and robust c…
Fast Global Localization on Neural Radiance Field
Mangyu Kong, Seongwon Lee, Jaewon Lee +1
Neural Radiance Fields (NeRF) presented a novel way to represent scenes, allowing for high-quality 3D reconstruction from 2D images. Following its remarkable achievements, global l…
GeomGS: LiDAR-Guided Geometry-Aware Gaussian Splatting for Robot Localization
Jaewon Lee, Mangyu Kong, Minseong Park +1
Mapping and localization are crucial problems in robotics and autonomous driving. Recent advances in 3D Gaussian Splatting (3DGS) have enabled precise 3D mapping and scene understa…
VIGS SLAM: IMU-based Large-Scale 3D Gaussian Splatting SLAM
Gyuhyeon Pak, Euntai Kim
Recently, map representations based on radiance fields such as 3D Gaussian Splatting and NeRF, which excellent for realistic depiction, have attracted considerable attention, leadi…
DGS-SLAM: Gaussian Splatting SLAM in Dynamic Environment
Mangyu Kong, Jaewon Lee, Seongwon Lee +1
We introduce Dynamic Gaussian Splatting SLAM (DGS-SLAM), the first dynamic SLAM framework built on the foundation of Gaussian Splatting. While recent advancements in dense SLAM hav…