计算机工程与应用 ›› 2025, Vol. 61 ›› Issue (8): 1-16.DOI: 10.3778/j.issn.1002-8331.2409-0330

• 热点与综述 • 上一篇    下一篇

视觉惯性联合标定发展综述

赵军阳,吕慎华,李永旭,祝慧鑫,张克凡   

  1. 火箭军工程大学 导弹工程学院,西安 710025
  • 出版日期:2025-04-15 发布日期:2025-04-15

Review of Development of Visual-Inertial Joint Calibration

ZHAO Junyang, LYU Shenhua, LI Yongxu, ZHU Huixin, ZHANG Kefan   

  1. Missile Engineering Institute, Rocket Force University of Engineering, Xi’an 710025, China
  • 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。

关键词: 相机和IMU联合, 联合标定方法, 数据融合与相互校正, 视觉惯性联合标定

Abstract: The joint use of cameras and IMU (inertial measurement unit) can fully leverage the complementary advantages of two sensors, enabling data fusion and mutual calibration. In recent years, a variety of intelligent joint calibration methods have emerged, however, there is a lack of unified summarization and analysis. Therefore, the visual-inertial joint calibration methods are classified and sorted in a unified way to analyze the application characteristics and limitations of various approaches, and provide a better choice foundation for the application or research of camera and IMU joint calibration methods. Firstly, this paper introduces the calibration parameters and principles for both the camera and IMU, discussing these from temporal and spatial perspectives. Secondly, it classifies and comparatively analyzes online and offline temporal calibration methods. From a spatial perspective, the paper categorizes calibration methods based on the distinct principles of IMU and camera calibration into four types: optimization-based calibration, decoupled model-based calibration, filtering-based calibration, and machine learning-based calibration, while evaluating the advantages and characteristics of each approach. Finally, to summarize the entire paper, it proposes the future development trends of joint calibration: spatiotemporal unified calibration, a greater variety of calibration toolkits, the expansion of machine learning applications, and multi-sensor joint calibration, among others.

Key words: joint use of cameras and IMU, joint calibration methods, data fusion and mutual calibration, visual-inertial joint calibration