9 citations · 14 across the 11 of their papers we have counts for
8 papers · 1 filter
Gaussian Semantic Field for One-shot LiDAR Global Localization
Pengyu Yin, Shenghai Yuan, Haozhi Cao +4
We present a one-shot LiDAR global localization algorithm featuring semantic disambiguation ability based on a lightweight tri-layered scene graph. While landmark semantic registra…
Large-Scale UWB Anchor Calibration and One-Shot Localization Using Gaussian Process
Shenghai Yuan, Boyang Lou, Thien-Minh Nguyen +7
Ultra-wideband (UWB) is gaining popularity with devices like AirTags for precise home item localization but faces significant challenges when scaled to large environments like seap…
Collaborative Graph Exploration with Reduced Pose-SLAM Uncertainty via Submodular Optimization
Ruofei Bai, Shenghai Yuan, Hongliang Guo +3
This paper considers the collaborative graph exploration problem in GPS-denied environments, where a group of robots are required to cover a graph environment while maintaining rel…
MCD: Diverse Large-Scale Multi-Campus Dataset for Robot Perception
Thien-Minh Nguyen, Shenghai Yuan, Thien Hoang Nguyen +8
Perception plays a crucial role in various robot applications. However, existing well-annotated datasets are biased towards autonomous driving scenarios, while unlabelled SLAM data…
Outram: One-shot Global Localization via Triangulated Scene Graph and Global Outlier Pruning
Pengyu Yin, Haozhi Cao, Thien-Minh Nguyen +4
One-shot LiDAR localization refers to the ability to estimate the robot pose from one single point cloud, which yields significant advantages in initialization and relocalization p…
LIO-GVM: an Accurate, Tightly-Coupled Lidar-Inertial Odometry with Gaussian Voxel Map
Xingyu Ji, Shenghai Yuan, Pengyu Yin +1
This letter presents an accurate and robust Lidar Inertial Odometry framework. We fuse LiDAR scans with IMU data using a tightly-coupled iterative error state Kalman filter for rob…
Segregator: Global Point Cloud Registration with Semantic and Geometric Cues
Pengyu Yin, Shenghai Yuan, Haozhi Cao +3
This paper presents Segregator, a global point cloud registration framework that exploits both semantic information and geometric distribution to efficiently build up outlier-robus…
CoBigICP: Robust and Precise Point Set Registration using Correntropy Metrics and Bidirectional Correspondence
Pengyu Yin, Di Wang, Shaoyi Du +3
In this paper, we propose a novel probabilistic variant of iterative closest point (ICP) dubbed as CoBigICP. The method leverages both local geometrical information and global nois…