Zichao Shu — accepted AI-conference papers

2026

MoCap2GT: A High-Precision Ground Truth Estimator for SLAM Benchmarking Based on Motion Capture and IMU Fusion

Zichao Shu, Shitao Bei, Jicheng Dai, Lijun Li, Zetao Chen, Jianyu Wang

RA-L 2026

Marker-based optical motion capture (MoCap) systems are widely used to provide ground truth (GT) trajectories for benchmarking SLAM algorithms. However, the accuracy of MoCap-based GT trajectories is mainly affected by two factors: spatiotemporal calibration errors between the MoCap system and the d

Cited by 1 Source ↗Scholar ↗

2024

A Spatiotemporal Hand-Eye Calibration for Trajectory Alignment in Visual(-Inertial) Odometry Evaluation

Zichao Shu, Lijun Li, Rui Wang, Zetao Chen

RA-L 2024

A common prerequisite for evaluating a visual(-inertial) odometry (VO/VIO) algorithm is to align the timestamps and the reference frame of its estimated trajectory with a reference ground-truth derived from a system of superior precision, such as a motion capture system. The trajectory-based alignme

Cited by 7 Source ↗Scholar ↗