Zhishuai Huang — accepted AI-conference papers

2026

AESF-LIO: Adaptive Error-State Fusion LiDAR-Inertial Odometry for Ground Vehicles in Structured Environments

Zhishuai Huang, Chao Sun, Jianghao Leng, Bo Wang

RA-L 2026

In LiDAR-based Simultaneous Localization and Mapping (SLAM) systems for vehicles, the point-to-plane Iterative Closest Point (ICP) method is widely used for scan matching. This approach incorporates all planar points into a single objective function for optimization, yet does not explicitly distingu

Cited by 0 Source ↗Scholar ↗

2024

SCE-LIO: An Enhanced LiDAR Inertial Odometry by Constructing Submap Constraints

Chao Sun, Zhishuai Huang, Bo Wang, Mancheng Xiao, Jianghao Leng, Jiajun Li

RA-L 2024

In LiDAR-based Simultaneous Localization and Mapping (SLAM) systems, loop closure detection is crucial for enhancing the accuracy of odometry. However, constraints from loop closure detection are only provided when a loop is detected and can only enhance odometry accuracy at specific moments. Theref

Cited by 3 Source ↗Scholar ↗