计算机工程与应用 ›› 2025, Vol. 61 ›› Issue (8): 1-16.DOI: 10.3778/j.issn.1002-8331.2409-0330
赵军阳,吕慎华,李永旭,祝慧鑫,张克凡
出版日期:
2025-04-15
发布日期:
2025-04-15
ZHAO Junyang, LYU Shenhua, LI Yongxu, ZHU Huixin, ZHANG Kefan
Online:
2025-04-15
Published:
2025-04-15
摘要: 相机和IMU联合可充分利用两个传感器的互补优势,实现数据融合与相互校正。近年来,更多智能化的联合标定方法不断出现,但缺少统一的归纳分析。为此,将视觉惯性联合标定方法统一分类整理,旨在分析各类方法的应用特点与局限性,为相机与IMU联合标定方法应用层面或是研究层面提供更好的选择基础。介绍了相机与IMU标定参数以及标定原理,并从时间、空间两个角度展开论述。分别对在线、离线的时间标定方法,进行分类归纳并作对比分析;从空间的角度,基于IMU和相机的标定方法原理不同将标定方法分为四类:基于优化的标定、基于解耦模型的标定、基于滤波的标定、基于机器学习的标定,深入分析每种方法的优势与局限性等。最后,总结全文并提出未来联合标定的发展趋势:时空统一标定、更多标定工具包、机器学习的扩展、多传感器联合标定等。
赵军阳, 吕慎华, 李永旭, 祝慧鑫, 张克凡. 视觉惯性联合标定发展综述[J]. 计算机工程与应用, 2025, 61(8): 1-16.
ZHAO Junyang, LYU Shenhua, LI Yongxu, ZHU Huixin, ZHANG Kefan. Review of Development of Visual-Inertial Joint Calibration[J]. Computer Engineering and Applications, 2025, 61(8): 1-16.
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