6 papers
GPA-VGGT:Adapting VGGT to Large Scale Localization by Self-Supervised Learning with Geometry and Physics Aware Loss
Yangfan Xu, Lilian Zhang, Xiaofeng He +3
Transformer-based general visual geometry frameworks have shown promising performance in camera pose estimation and 3D scene understanding. Recent advancements in Visual Geometry G…
SP-VINS: A Hybrid Stereo Visual Inertial Navigation System based on Implicit Environmental Map
Xueyu Du, Lilian Zhang, Fuan Duan +4
Filter-based visual inertial navigation system (VINS) has attracted mobile-robot researchers for the good balance between accuracy and efficiency, but its limited mapping quality h…
VGC-RIO: A Tightly Integrated Radar-Inertial Odometry with Spatial Weighted Doppler Velocity and Local Geometric Constrained RCS Histograms
Jianguang Xiang, Xiaofeng He, Zizhuo Chen +3
Recent advances in 4D radar-inertial odometry have demonstrated promising potential for autonomous lo calization in adverse conditions. However, effective handling of sparse and no…
SP-VIO: Robust and Efficient Filter-Based Visual Inertial Odometry with State Transformation Model and Pose-Only Visual Description
Xueyu Du, Lilian Zhang, Chengjun Ji +5
Due to the advantages of high computational efficiency and small memory requirements, filter-based visual inertial odometry (VIO) has a good application prospect in miniaturized an…
SLAM in the Dark: Self-Supervised Learning of Pose, Depth and Loop-Closure from Thermal Images
Yangfan Xu, Qu Hao, Lilian Zhang +4
Visual SLAM is essential for mobile robots, drone navigation, and VR/AR, but traditional RGB camera systems struggle in low-light conditions, driving interest in thermal SLAM, whic…
PO-MSCKF: An Efficient Visual-Inertial Odometry by Reconstructing the Multi-State Constrained Kalman Filter with the Pose-only Theory
Xueyu Du, Lilian Zhang, Ruochen Liu +3
Efficient Visual-Inertial Odometry (VIO) is crucial for payload-constrained robots. Though modern optimization-based algorithms have achieved superior accuracy, the MSCKF-based VIO…