@inproceedings{d5dad59b0c244368ab84c4d0812310ad,
title = "Fine Registration of Low-Resolution Infrared Camera and Low-Beam LiDAR",
abstract = "Extrinsic calibration forms the foundation for data fusion with multi-sensors. However, challenges arise for low resolution infrared cameras and low-beams LiDAR extrinsic calibration, which are resulted by technology and cost considerations. Traditional calibration methods using chessboard patterns are no longer suitable for these two kinds of sensors. Therefore, this paper proposes a novel extrinsic calibration method based on special feature points from infrared images and LiDAR point clouds. The framework involves two main steps: an initial extrinsic guess using Perspective-n-Points and refinement of extrinsic parameters through similarity optimization. This method impressively shortens the calibration time compared to manual calibration and improves accuracy and robustness. In order to verify the efficacy of this approach, practical experiments were carried out using authentic datasets acquired from a physical unmanned vehicle.",
keywords = "extrinsic calibration, infrared camera, low-beam LiDAR, perspective- n - Points, registration",
author = "Leyun Hu and Chao Wei and Yang Xu and Zhe Zhang and Luxing Li and Xinhao Qian",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Unmanned Systems, ICUS 2023 ; Conference date: 13-10-2023 Through 15-10-2023",
year = "2023",
doi = "10.1109/ICUS58632.2023.10318405",
language = "English",
series = "Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "925--930",
editor = "Rong Song",
booktitle = "Proceedings of 2023 IEEE International Conference on Unmanned Systems, ICUS 2023",
address = "United States",
}