文献类型：期刊文献

英文题名：Research Progress on Joint Calibration Technology of LiDAR and Camera

作者：Zhao, Zhe[1,2]; Liu, Yuansheng[1,2]

第一作者：Zhao, Zhe

机构：[1] Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, 100101, China; [2] College of Robotics, Beijing Union University, Beijing, 100101, China

第一机构：北京联合大学北京市信息服务工程重点实验室

年份：2026

卷号：2631 CCIS

起止页码：51-66

外文期刊名：Communications in Computer and Information Science

收录：EI(收录号：20260419959706)

语种：英文

外文关键词：Autonomous vehicles - Calibration - Deep learning - Environmental technology - Information theory - Reliability theory - Remote sensing

摘要：In the context of autonomous driving, single sensors have the disadvantage of incomplete data and information, so accurate calibration of multiple sensors has gradually become an important foundation for later work. This article systematically reviews the joint calibration technology of LiDAR and camera. Based on a brief introduction to the internal parameter calibration methods of LiDAR and camera, the main methods of joint calibration are analyzed, including target based calibration methods and target free calibration methods. The target based calibration method has the characteristics of high accuracy and strong reliability, but it relies on the production of calibration objects and manual feature extraction; Objective free calibration methods have higher levels of automation and development potential, among which feature-based, information theory based, motion based, and deep learning based calibration methods each have their own advantages and limitations. By comparing and analyzing the principles, characteristics, and applicable scenarios of various methods, this article aims to provide technical selection references for researchers in related fields, and hopes to combine the advantages of multiple methods in the future to further improve the performance and practicality of LiDAR camera joint calibration technology, in order to meet the strict requirements of environmental perception accuracy and efficiency in applications such as autonomous driving. ? The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.