Jingnan Liu — accepted AI-conference papers

2023

FF-LINS: A Consistent Frame-to-Frame Solid-State-LiDAR-Inertial State Estimator

Hailiang Tang, Tisheng Zhang, Xiaoji Niu, Liqiang Wang, Linfu Wei, Jingnan Liu

RA-L 2023

Most of the existing LiDAR-inertial navigation systems are based on frame-to-map registrations, leading to inconsistency in state estimation. The newest solid-state LiDAR with a non-repetitive scanning pattern makes it possible to achieve a consistent LiDAR-inertial estimator by employing a frame-to

Cited by 14 Source ↗code ↗Scholar ↗

2023

IC-GVINS: A Robust, Real-Time, INS-Centric GNSS-Visual-Inertial Navigation System

Xiaoji Niu, Hailiang Tang, Tisheng Zhang, Jing Fan, Jingnan Liu

RA-L 2023

Visual navigation systems are susceptible to complex environments, while inertial navigation systems (INS) are not affected by external factors. Hence, we present IC-GVINS, a robust, real-time, INS-centric global navigation satellite system (GNSS)-visual-inertial navigation system to fully utilize t

Cited by 91 Source ↗Scholar ↗