Rongjun Ding — accepted AI-conference papers

2023

Versatile Multi-LiDAR Accurate Self-Calibration System Based on Pose Graph Optimization

Dengxiang Chang, Runbang Zhang, Shengjie Huang, Manjiang Hu, Rongjun Ding, Xiaohui Qin

RA-L 2023

Multi-LiDAR systems provide all-round perception of the surrounding environment from different directions simultaneously, significantly enhancing the sensing range and density of measurements. Accurate extrinsic calibration is a key prerequisite for data fusion of multi-LiDAR systems. However, exist

Cited by 11 Source ↗Scholar ↗

2023

WiCRF: Weighted Bimodal Constrained LiDAR Odometry and Mapping With Robust Features

Dengxiang Chang, Runbang Zhang, Shengjie Huang, Manjiang Hu, Rongjun Ding, Xiaohui Qin

RA-L 2023

Accurate localization is a fundamental capability of autonomous driving systems, and LiDAR has been widely used for localization systems in recent years due to its high reliability and accuracy. In this paper, we propose a robust and accurate LiDAR SLAM, which innovates feature point extraction and

Cited by 10 Source ↗Scholar ↗