3 papers
cs.RO2025
M2UD: A Multi-model, Multi-scenario, Uneven-terrain Dataset for Ground Robot with Localization and Mapping Evaluation
Yanpeng Jia, Shiyi Wang, Shiliang Shao +3
Ground robots play a crucial role in inspection, exploration, rescue, and other applications. In recent years, advancements in LiDAR technology have made sensors more accurate, lig…
cs.RO2024
TRLO: An Efficient LiDAR Odometry with 3D Dynamic Object Tracking and Removal
Yanpeng Jia, Ting Wang, Xieyuanli Chen +1
Simultaneous state estimation and mapping is an essential capability for mobile robots working in dynamic urban environment. The majority of existing SLAM solutions heavily rely on…
cs.RO2024
CAD-Mesher: A Convenient, Accurate, Dense Mesh-based Mapping Module in SLAM for Dynamic Environments
Yanpeng Jia, Fengkui Cao, Ting Wang +3
Most LiDAR odometry and SLAM systems construct maps in point clouds, which are discrete and sparse when zoomed in, making them not directly suitable for navigation. Mesh maps repre…