@inproceedings{92a7839e68274338937c2fc2beff37c1,
title = "A Two-Step 3D LiDAR-Camera Calibration Method Using AR Tags",
abstract = "In this paper, we present a simple and precise method of 3D LiDAR-Camera calibration. Our algorithm introduces a two-step method which contains coarse estimating and accurate optimization. The coarse estimating involves solving a Random Sample Consensus (RANSAC) version of Perspective-n-Point (PnP) problem by the means of matching 2D-3D point correspondences in LiDAR and camera frame. Then we implement a RANSAC algorithm on LiDAR points for plane fitting and easily obtain multiple planes parameters in camera frame with Augmented Reality (AR) tags. Finally, the accurate optimization utilizes plane correspondences in camera and LiDAR frame for a precise result. Our proposed method just needs one frame measurement and the physical size of AR tags. A succession of experiments is conducted to show the validity of our algorithm.",
keywords = "3D LiDAR, 3D LiDAR-Camera calibration, Camera",
author = "Jiaxin Luo and Wenhui Cui and Xiaorong Shen",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 17th Chinese Intelligent Systems Conference, CISC 2021 ; Conference date: 16-10-2021 Through 17-10-2021",
year = "2022",
doi = "10.1007/978-981-16-6324-6\_45",
language = "英语",
isbn = "9789811663239",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "443--452",
editor = "Yingmin Jia and Weicun Zhang and Yongling Fu and Zhiyuan Yu and Song Zheng",
booktitle = "Proceedings of 2021 Chinese Intelligent Systems Conference - Volume II",
address = "德国",
}