Calibration of external relation between imaging laser radar and camera

HU Feng; HU Chun-sheng; WANG Xing-shu; JIAO Hong-wei

doi:10.3788/OPE.20111904.0938

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Calibration of external relation between imaging laser radar and camera

Article | 更新时间：2020-08-12

- Calibration of external relation between imaging laser radar and camera
- Optics and Precision Engineering Vol. 19, Issue 4, Pages: 938-943(2011)
- 作者机构：
  
  国防科技大学光电科学与工程学院,湖南长沙 410073
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20111904.0938
  CLC： TP73;TN958.98
- Received：28 July 2010，
  
  Revised：25 August 2010，
  
  Published Online：26 April 2011，
  
  Published：26 April 2011
- 稿件说明：
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胡峰, 胡春生, 王省书, 焦宏伟. 成像激光雷达与摄像机外部位置关系的标定[J]. 光学精密工程, 2011,19(4): 938-943

HU Feng, HU Chun-sheng, WANG Xing-shu, JIAO Hong-wei. Calibration of external relation between imaging laser radar and camera[J]. Editorial Office of Optics and Precision Engineering, 2011,19(4): 938-943
胡峰, 胡春生, 王省书, 焦宏伟. 成像激光雷达与摄像机外部位置关系的标定[J]. 光学精密工程, 2011,19(4): 938-943 DOI： 10.3788/OPE.20111904.0938.

HU Feng, HU Chun-sheng, WANG Xing-shu, JIAO Hong-wei. Calibration of external relation between imaging laser radar and camera[J]. Editorial Office of Optics and Precision Engineering, 2011,19(4): 938-943 DOI： 10.3788/OPE.20111904.0938.

摘要

为了确定成像激光雷达与摄像机两种不同传感器的相互位置参数

建立两者的一一映射关系

在现有激光雷达与摄像机外部参数标定方法的基础上

通过引入奇异值分解技术

对现有标定算法进行了改进。首先

介绍了改进标定算法的基本原理

采用奇异值分解方法求解超定方程组

得到成像激光雷达坐标系与摄像机坐标系之间的投影变换矩阵。为了降低噪声影响

使用非线性优化算法优化初值。然后

利用自制的立体标定靶进行了标定实验

并使用已标定出的结果对成像激光雷达图像和摄像机图像进行了融合实验来验证提出的标定算法。实验结果表明

该算法平均标定精度达到13.33 m

标准差为7.49 m

比原始算法提高了1倍

基本满足图像融合对标定精度的一般要求。

Abstract

In order to determine the position parameters between an imaging laser radar and a camera and to establish the mapping relation between them

an improved calibration method was proposed based on Singular Value Decomposition (SVD) and present calibration arithmetics. Firstly

the calibration principle was introduced

then the SVD was used to solve overdetermined equations to obtain the projective transformation matrixes of laser radar coordinates and camera coordinates. To reduce the influence of noise

the initial result was optimized with the Levenberg-Marquardt algorithm. Then

the calibration experiments were carried out with a 3D calibration object and the image fusion experiment was performed according to the calibration result to validate the improved calibration arithmetic. Experimental results indicate that the average calibration precision of the arithmetic is 13.33 m and the standard error is 7.49 m

which is doubled that of the original arithmetic. The precision of the improved calibration arithmetic can meet the requirement of image fusion.

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references

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