基于单目视觉的GPS辅助相机外参数标定

吴修振; 刘刚; 于凤全; 张源原

doi:10.3788/OPE.20172508.2252

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基于单目视觉的GPS辅助相机外参数标定

信息科学 | 更新时间：2020-07-07

- 基于单目视觉的GPS辅助相机外参数标定
- Calibration of camera extrinsic parameters based on monocular visual with GPS assistant
- 光学精密工程 2017年25卷第8期页码：2252-2258
- 作者机构：
  
  1.海军航空工程学院控制工程系, 山东烟台 264001
  2.海军航空兵学院空中领航系, 辽宁葫芦岛 125000
- 作者简介：
  
  吴修振(1988-), 男, 山东莱芜人, 博士研究生, 2012年于海军航空工程学院获得硕士学位, 主要从事导航、制导与控制方面研究。E-mail:wxz_lucky@163.com
  [ "刘刚(1985-), 男, 云南昆明人, 博士, 讲师, 2015年于清华大学获得博士学位, 主要研究方向为导航、制导与控制。E-mail:348651165@qq.com" ]
- 基金信息：
  
  国家自然科学基金资助项目(61004002)
- DOI：10.3788/OPE.20172508.2252
  中图分类号： V249.329;TP391
- 收稿日期：2016-12-23，
  
  录用日期：2017-3-30，
  
  纸质出版日期：2017-08-25
- 稿件说明：
移动端阅览
吴修振, 刘刚, 于凤全, 等. 基于单目视觉的GPS辅助相机外参数标定[J]. 光学精密工程, 2017,25(8):2252-2258.

Xiu-zhen WU, Gang LIU, Feng-quan YU, et al. Calibration of camera extrinsic parameters based on monocular visual with GPS assistant[J]. Optics and precision engineering, 2017, 25(8): 2252-2258.
吴修振, 刘刚, 于凤全, 等. 基于单目视觉的GPS辅助相机外参数标定[J]. 光学精密工程, 2017,25(8):2252-2258. DOI： 10.3788/OPE.20172508.2252.

Xiu-zhen WU, Gang LIU, Feng-quan YU, et al. Calibration of camera extrinsic parameters based on monocular visual with GPS assistant[J]. Optics and precision engineering, 2017, 25(8): 2252-2258. DOI： 10.3788/OPE.20172508.2252.

摘要

针对机器人视觉系统外参数标定的问题，提出了基于单目视觉ORB-SLAM的差分GPS辅助相机外参数标定方法。分析了单目视觉ORB-SLAM和GPS（Global Position System）定位数据之间的相似关系，建立了相机外参数标定的非线性最小二乘模型。基于随机采样一致性（RANSAC），通过三点法求得模型的初始解。设计了Levenberg-Marquardt（LM）迭代算法求解出最优解，从而得到了最优的相机相对位置和姿态参数。最后，对提出的方法进行仿真和跑车试验验证。结果表明：在试验半径为50 m时，所设计标定方法的姿态标定精度可达0.1°，位置标定精度可达0.2%。该方法标定过程简单实用，不需要外界环境的先验信息和人工干预，具有很高的精度和显著的应用价值。

Abstract

An extrinsic parameter calibration method with differential GPS(Global Position System) assistant based on monocular visual ORB-SLAM(ORB-Simullaneous Location and Mapping) was proposed aimed at extrinsic parameter calibration problem of a robot vision system. Nonlinear least square models of extrinsic parameter calibration were established based on analyzing the similarity relationship between monocular visual ORB-SLAM and GPS positioning data. The initial solution of model was obtained by three-point method based on Random Sample Consensus (RANSAC)

and then an optimal solution was obtained by designing Levenberg-Marquardt (LM) iterative algorithm. Thus optimal relative position and pose parameters of a camera were obtained. Simulation and traffic-running experimental verification was performed for proposed methods. The result indicates that when experimental radius was 50 m

the pose calibration precision of designed calibration method could reach 0.1° and position calibration precision could reach 0.2%. It concludes that the calibration process of the method is simple and practical. It does not need prior information of external environment and manual intervention

and has high precision and a significant application value.

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references

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