9 citations · 13 across the 13 of their papers we have counts for
14 papers
MomentBA: Second-order Spatial Moments for Anisotropic Correspondence Uncertainty in Differentiable Bundle Adjustment
Yuqing Wang, Xiaoji Niu, Yan Wang +3
Most existing visual odometry (VO) systems treat feature correspondences as deterministic measurements or assign uniform uncertainty, ignoring the inherent localization ambiguity o…
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…
ViBA: Implicit Bundle Adjustment with Geometric and Temporal Consistency for Robust Visual Matching
Xiaoji Niu, Yuqing Wang, Yan Wang +2
Most existing image keypoint detection and description methods rely on datasets with accurate pose and depth annotations, limiting scalability and generalization, and often degradi…
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…
DogLegs: Robust Proprioceptive State Estimation for Legged Robots Using Multiple Leg-Mounted IMUs
Yibin Wu, Jian Kuang, Shahram Khorshidi +4
Robust and accurate proprioceptive state estimation of the main body is crucial for legged robots to execute tasks in extreme environments where exteroceptive sensors, such as LiDA…
Wheel-GINS: A GNSS/INS Integrated Navigation System with a Wheel-mounted IMU
Yibin Wu, Jian Kuang, Xiaoji Niu +3
A long-term accurate and robust localization system is essential for mobile robots to operate efficiently outdoors. Recent studies have shown the significant advantages of the whee…
MR-ULINS: A Tightly-Coupled UWB-LiDAR-Inertial Estimator with Multi-Epoch Outlier Rejection
Tisheng Zhang, Man Yuan, Linfu Wei +3
The LiDAR-inertial odometry (LIO) and the ultra-wideband (UWB) have been integrated together to achieve driftless positioning in global navigation satellite system (GNSS)-denied en…
MSC-LIO: An MSCKF-Based LiDAR-Inertial Odometry with Same-Plane Cluster Tracking
Tisheng Zhang, Man Yuan, Linfu Wei +2
The multi-state constraint Kalman filter (MSCKF) has been proven to be more efficient than graph optimization for visual-based odometry while with similar accuracy. However, it has…
BA-LINS: A Frame-to-Frame Bundle Adjustment for LiDAR-Inertial Navigation
Hailiang Tang, Tisheng Zhang, Liqiang Wang +2
Bundle Adjustment (BA) has been proven to improve the accuracy of the LiDAR mapping. However, the BA method has not yet been properly employed in a dead-reckoning navigation system…
SE-LIO: Semantics-enhanced Solid-State-LiDAR-Inertial Odometry for Tree-rich Environments
Tisheng Zhang, Linfu Wei, Hailiang Tang +3
In this letter, we propose a semantics-enhanced solid-state-LiDAR-inertial odometry (SE-LIO) in tree-rich environments. Multiple LiDAR frames are first merged and compensated with…