40 citations · 101 across the 28 of their papers we have counts for
6 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…
Enhancing Scene Coordinate Regression with Efficient Keypoint Detection and Sequential Information
Kuan Xu, Zeyu Jiang, Haozhi Cao +3
Scene Coordinate Regression (SCR) is a visual localization technique that utilizes deep neural networks (DNN) to directly regress 2D-3D correspondences for camera pose estimation.…
AIR-Embodied: An Efficient Active 3DGS-based Interaction and Reconstruction Framework with Embodied Large Language Model
Zhenghao Qi, Shenghai Yuan, Fen Liu +4
Recent advancements in 3D reconstruction and neural rendering have enhanced the creation of high-quality digital assets, yet existing methods struggle to generalize across varying…
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…
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…