5 citations · 11 across the 7 of their papers we have counts for
12 papers
RING#: PR-by-PE Global Localization with Roto-translation Equivariant Gram Learning
Sha Lu, Xuecheng Xu, Yuxuan Wu +4
Global localization using onboard perception sensors, such as cameras and LiDARs, is crucial in autonomous driving and robotics applications when GPS signals are unreliable. Most a…
DeepRING: Learning Roto-translation Invariant Representation for LiDAR based Place Recognition
Sha Lu, Xuecheng Xu, Li Tang +2
LiDAR based place recognition is popular for loop closure detection and re-localization. In recent years, deep learning brings improvements to place recognition by learnable featur…
RING++: Roto-translation Invariant Gram for Global Localization on a Sparse Scan Map
Xuecheng Xu, Sha Lu, Jun Wu +5
Global localization plays a critical role in many robot applications. LiDAR-based global localization draws the community's focus with its robustness against illumination and seaso…
Translation Invariant Global Estimation of Heading Angle Using Sinogram of LiDAR Point Cloud
Xiaqing Ding, Xuecheng Xu, Sha Lu +6
Global point cloud registration is an essential module for localization, of which the main difficulty exists in estimating the rotation globally without initial value. With the aid…
HiTMap: A Hierarchical Topological Map Representation for Navigation in Unknown Environments
Xuecheng Xu, Cheng Wang, Yue Wang +1
The ability to autonomously navigate in unknown environments is important for mobile robots. The map is the core component to achieve this. Most map representations rely on drift-f…
Learn to Differ: Sim2Real Small Defection Segmentation Network
Zexi Chen, Zheyuan Huang, Yunkai Wang +3
Recent studies on deep-learning-based small defection segmentation approaches are trained in specific settings and tend to be limited by fixed context. Throughout the training, the…