9 citations · 13 across the 13 of their papers we have counts for
12 papers · 1 filter
DINS-IO: Learned Inertial Odometry via Differentiable INS Consistency
Hao Qiao, Yan Wang, Jian Kuang +1
The training of learned inertial odometry depends on dense, high-precision position ground truth from motion capture, visual-inertial odometry or SLAM, which is costly and hard to…
PA-LVIO: Real-Time LiDAR-Visual-Inertial Odometry and Mapping with Pose-Only Bundle Adjustment
Hailiang Tang, Tisheng Zhang, Liqiang Wang +3
Real-time LiDAR-visual-inertial odometry and mapping is crucial for navigation and planning tasks in intelligent transportation systems. This study presents a pose-only bundle adju…
MoE-Based Learned Inertial Odometry for Bicycle Localization
Hao Qiao, Yan Wang, Shuo Yang +3
GNSS suffers from multipath errors in urban canyons, making reliable bicycle localization difficult. Hand-crafted inertial alternatives, such as cycling dead reckoning and nonholon…
i2Nav-Robot: A Large-Scale Indoor-Outdoor Robot Dataset for Multi-Sensor Fusion Navigation
Hailiang Tang, Tisheng Zhang, Liqiang Wang +9
Accurate and reliable navigation is crucial for autonomous unmanned ground vehicles (UGVs). However, current UGV datasets fall short in meeting the demands for advancing navigation…
MSCEKF-MIO: Magnetic-Inertial Odometry Based on Multi-State Constraint Extended Kalman Filter
Jiazhu Li, Jian Kuang, Xiaoji Niu
To overcome the limitation of existing indoor odometry technologies which often cannot simultaneously meet requirements for accuracy cost-effectiveness, and robustness-this paper p…
SELC: Self-Supervised Efficient Local Correspondence Learning for Low Quality Images
Yuqing Wang, Yan Wang, Hailiang Tang +1
Accurate and stable feature matching is critical for computer vision tasks, particularly in applications such as Simultaneous Localization and Mapping (SLAM). While recent learning…