30 citations · 58 across the 17 of their papers we have counts for
17 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…
Leveraging BEV Representation for 360-degree Visual Place Recognition
Xuecheng Xu, Yanmei Jiao, Sha Lu +3
This paper investigates the advantages of using Bird's Eye View (BEV) representation in 360-degree visual place recognition (VPR). We propose a novel network architecture that util…
A Survey on Global LiDAR Localization: Challenges, Advances and Open Problems
Huan Yin, Xuecheng Xu, Sha Lu +5
Knowledge about the own pose is key for all mobile robot applications. Thus pose estimation is part of the core functionalities of mobile robots. Over the last two decades, LiDAR s…
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…
DPCN++: Differentiable Phase Correlation Network for Versatile Pose Registration
Zexi Chen, Yiyi Liao, Haozhe Du +5
Pose registration is critical in vision and robotics. This paper focuses on the challenging task of initialization-free pose registration up to 7DoF for homogeneous and heterogeneo…
One RING to Rule Them All: Radon Sinogram for Place Recognition, Orientation and Translation Estimation
Sha Lu, Xuecheng Xu, Huan Yin +3
LiDAR-based global localization is a fundamental problem for mobile robots. It consists of two stages, place recognition and pose estimation, which yields the current orientation a…
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…
Learning Interpretable BEV Based VIO without Deep Neural Networks
Zexi Chen, Haozhe Du, Xuecheng Xu +3
Monocular visual-inertial odometry (VIO) is a critical problem in robotics and autonomous driving. Traditional methods solve this problem based on filtering or optimization. While…
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…
Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning
Huan Yin, Xuecheng Xu, Yue Wang +1
Place recognition is critical for both offline mapping and online localization. However, current single-sensor based place recognition still remains challenging in adverse conditio…
CORAL: Colored structural representation for bi-modal place recognition
Yiyuan Pan, Xuecheng Xu, Weijie Li +3
Place recognition is indispensable for a drift-free localization system. Due to the variations of the environment, place recognition using single-modality has limitations. In this…
PREGAN: Pose Randomization and Estimation for Weakly Paired Image Style Translation
Zexi Chen, Jiaxin Guo, Xuecheng Xu +3
Utilizing the trained model under different conditions without data annotation is attractive for robot applications. Towards this goal, one class of methods is to translate the ima…
DiSCO: Differentiable Scan Context with Orientation
Xuecheng Xu, Huan Yin, Zexi Chen +2
Global localization is essential for robot navigation, of which the first step is to retrieve a query from the map database. This problem is called place recognition. In recent yea…
Deep Phase Correlation for End-to-End Heterogeneous Sensor Measurements Matching
Zexi Chen, Xuecheng Xu, Yue Wang +1
The crucial step for localization is to match the current observation to the map. When the two sensor modalities are significantly different, matching becomes challenging. In this…
Collaborative Localization of Aerial and Ground Mobile Robots through Orthomosaic Map
Xuecheng Xu, Zexi Chen, Jiaxin Guo +3
With the deepening of research on the SLAM system, the possibility of cooperative SLAM with multi-robots has been proposed. This paper presents a map matching and localization appr…