Liqiang Wang

Orcid: 0000-0002-2437-3077

Affiliations:
  • Wuhan University, GNSS Research Center, China


According to our database1, Liqiang Wang authored at least 12 papers between 2023 and 2026.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
PA-LVIO: Real-Time LiDAR-Visual-Inertial Odometry and Mapping with Pose-Only Bundle Adjustment.
CoRR, March, 2026

Ultra-Tightly Coupled GNSS/INS/Vision Integrated System for Centimeter-Level Vehicle Positioning in Challenging Environments.
IEEE Trans. Intell. Transp. Syst., February, 2026

PLPO-KF: A Unified Pose-Only Kalman Filter With Point-Line Features for Visual-Inertial Odometry.
IEEE Internet Things J., 2026

2025
i2Nav-Robot: A Large-Scale Indoor-Outdoor Robot Dataset for Multi-Sensor Fusion Navigation and Mapping.
CoRR, August, 2025

BA-LINS: A Frame-to-Frame Bundle Adjustment for LiDAR-Inertial Navigation.
IEEE Trans. Intell. Transp. Syst., May, 2025

SE-LIO: Semantic-Enhanced Solid-State-LiDAR-Inertial Odometry for Tree-Rich Environments.
IEEE Trans. Instrum. Meas., 2025

PO-KF: A Pose-Only Representation-Based Kalman Filter for Visual Inertial Odometry.
IEEE Internet Things J., 2025

2024
Enhancing Visual Navigation Performance by Prior Pose-Guided Active Feature Points Distribution.
IEEE Trans. Instrum. Meas., 2024

2023
FF-LINS: A Consistent Frame-to-Frame Solid-State-LiDAR-Inertial State Estimator.
IEEE Robotics Autom. Lett., December, 2023

LE-VINS: A Robust Solid-State-LiDAR-Enhanced Visual-Inertial Navigation System for Low-Speed Robots.
IEEE Trans. Instrum. Meas., 2023

SE-LIO: Semantics-enhanced Solid-State-LiDAR-Inertial Odometry for Tree-rich Environments.
CoRR, 2023

PO-VINS: An Efficient Pose-Only LiDAR-Enhanced Visual-Inertial State Estimator.
CoRR, 2023


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