Kai Luan — accepted AI-conference papers

2025

RLCNet: A Novel Deep Feature-Matching-Based Method for Online Target-Free Radar-LiDAR Calibration

Kai Luan, Chenghao Shi, Xieyuanli Chen, Rui Fan, Zhiqiang Zheng, Huimin Lu

ICRA 2025

While millimeter-wave radars are widely used in robotics and autonomous driving, extrinsic calibration with other sensors remains challenging due to the sparsity and uncertainty of radar point clouds. In this paper, we propose a novel deep feature-matching-based online extrinsic calibration approach

Cited by 0 Source ↗code ↗Scholar ↗

2024

Diffusion-Based Point Cloud Super-Resolution for mmWave Radar Data

Kai Luan, Chenghao Shi, Neng Wang, Yuwei Cheng, Huimin Lu, Xieyuanli Chen

ICRA 2024poster

The millimeter-wave radar sensor maintains stable performance under adverse environmental conditions, making it a promising solution for all-weather perception tasks, such as outdoor mobile robotics. However, the radar point clouds are relatively sparse and contain massive ghost points, which greatl…

Cited by 7 Source ↗Scholar ↗