2 citations · 4 across the 5 of their papers we have counts for
5 papers · 1 filter
RICH-SLAM: Radar SLAM with Incremental and Continuous Hilbert Mapping
Bingbing Zhang, Huan Yin, Yang Xu +4
Simultaneous localization and mapping using radar sensors has gained increasing attention due to radar's inherent robustness to adverse weather and lighting conditions. However, ra…
SLABIM: A SLAM-BIM Coupled Dataset in HKUST Main Building
Haoming Huang, Zhijian Qiao, Zehuan Yu +4
Existing indoor SLAM datasets primarily focus on robot sensing, often lacking building architectures. To address this gap, we design and construct the first dataset to couple the S…
SLIM: Scalable and Lightweight LiDAR Mapping in Urban Environments
Zehuan Yu, Zhijian Qiao, Wenyi Liu +2
LiDAR point cloud maps are extensively utilized on roads for robot navigation due to their high consistency. However, dense point clouds face challenges of high memory consumption…
Speak the Same Language: Global LiDAR Registration on BIM Using Pose Hough Transform
Zhijian Qiao, Haoming Huang, Chuhao Liu +4
Light detection and ranging (LiDAR) point clouds and building information modeling (BIM) represent two distinct data modalities in the fields of robot perception and construction.…
Incorporating Point Uncertainty in Radar SLAM
Yang Xu, Qiucan Huang, Shaojie Shen +1
Radar SLAM is robust in challenging conditions, such as fog, dust, and smoke, but suffers from the sparsity and noisiness of radar sensing, including speckle noise and multipath ef…