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1 安装依赖

1、安装依赖

rosdep install camera_calibration

注意：

我的系统是Mint19.04，因此安装的过程中会报如下错误：

(base) shl@zhihui-mint:~$ rosdep install camera_calibration
ERROR: the following packages/stacks could not have their rosdep keys resolved
to system dependencies:
camera_calibration: Unsupported OS [mint]
(base) shl@zhihui-mint:~$

错误解决方式：查看Mint对应的Ubuntu的版本号，然后再安装：

1）查看Mint对应的Ubuntu版本号

cat /etc/os-release

(base) shl@zhihui-mint:~$ cat /etc/os-release
NAME="Linux Mint"
VERSION="19.3 (Tricia)"
ID=linuxmint
ID_LIKE=ubuntu
PRETTY_NAME="Linux Mint 19.3"
VERSION_ID="19.3"
HOME_URL="https://www.linuxmint.com/"
SUPPORT_URL="https://forums.linuxmint.com/"
BUG_REPORT_URL="http://linuxmint-troubleshooting-guide.readthedocs.io/en/latest/"
PRIVACY_POLICY_URL="https://www.linuxmint.com/"
VERSION_CODENAME=tricia
UBUNTU_CODENAME=bionic
(base) shl@zhihui-mint:~$ rosdep install camera_calibration --os=ubuntu:bionic
#All required rosdeps installed successfully
(base) shl@zhihui-mint:~$

2）安装camera_calibration包

rosdep install camera_calibration --os=ubuntu:bionic

安装：camera-calibration包，我的Mint19.3对应的是Ubuntu18.3，对应的ros的版本号为：melodic

因此Ubuntu18.04对应的ros版本号为melodic：

sudo apt install ros-melodic-camera-calibration

2 运行标定节点

2.1 rocore

roscore

roscore

2.2 启动摄像头驱动程序

1、关于启动摄像头驱动程序，参考这篇博客，我这里使用的是usb_cam驱动

roslaunch usb_cam-test.launch

2、确保单目相机正在通过ROS发布图像。列出主题来检查图像是否已发布：

rostopic list

这会显示所有已发布的主题，检查是否有image_raw主题。大多数ROS相机驱动程序提供的默认主题是

/camera/camera_info
/camera/image_raw

我自己的查看如下：

(base) shl@zhihui-mint:~$ rostopic list
/rosout
/rosout_agg
/usb_cam/camera_info
/usb_cam/image_raw
(base) shl@zhihui-mint:~$

注意：

我的显示的名字是/usb_cam，这个在下面加载图像主题的时候需要注意

2.3 加载将要标定的图像主题

1、首先找到你的标定程序cameracalibrator.py

(base) shl@zhihui-mint:~$ find /opt/ros -name cameracalibrator.py
/opt/ros/melodic/lib/camera_calibration/cameracalibrator.py
(base) shl@zhihui-mint:~$

2、进入标定程序目录

cd /opt/ros/melodic/lib/camera_calibration

3、加载将要标定的图像主题

rosrun camera_calibration cameracalibrator.py --size 8x6 --square 0.108 image:=/usb_cam/image_raw camera:=/usb_cam

(base) shl@zhihui-mint:/opt/ros/melodic/lib/camera_calibration$ rosrun camera_calibration cameracalibrator.py --size 8x6 --square 0.108 image:=/usb_cam/image_raw camera:=/usb_cam
Waiting for service /usb_cam/set_camera_info ...
OK

此命令运行标定结点的python脚本，其中：（1）--size 8x6：为当前标定板的大小（如果你的棋盘格的小方格的个数是9x7，那么我们这里就写--size 8x6，因我检测棋盘格是内部小正方形角点的交点！）（2）--square 0.108：为每个小棋盘格的边长，单位是米（3）image:=/camera/image_raw：标定当前订阅图像来源自名为/camera/image_raw的topic （4）camera:=/camera：为摄像机名

注意1：

你需要使用rostopic list查看自己的的订阅名，我使用的是USB摄像机，因此用的是上面的命令，如果你不可能就需要用下面的命令

rosrun camera_calibration cameracalibrator.py --size 8x6 --square 0.108 image:=/camera/image_raw camera:=/camera

注意2：

如果你运行报错：Service not found，这原因很可能是因为你的订阅名指定错误了

base) shl@zhihui-mint:/opt/ros/melodic/lib/camera_calibration$ rosrun camera_calibration cameracalibrator.py --size 8x6 --square 0.108 image:=/camera/image_raw camera:=/camera
Waiting for service /camera/set_camera_info ...
Service not found
(base) shl@zhihui-mint:/opt/ros/melodic/lib/camera_calibration$

3 开始标定

3.1 打印一张棋盘格图片

可以从这里获取，下面给出opencv官网给出的一张9*6内角点棋盘格，格子大小为26mm：

opencv ——pattern.png

3.2 具体标定过程

8×6和0.108是官方给出的标定板的尺寸，请根据自己的修改比如我的是8×6和0.0216（参考）

为了得到一个好的标定结果，应该使得标定板尽量出现在摄像头视野的各个位置里：如标定板出现在视野中的左边，右边，上边和下边，标定板既有倾斜的，也有水平的.所以多动一动～

界面中的

x：表示标定板在视野中的左右位置。

y：表示标定板在视野中的上下位置。

size：标定板在占视野的尺寸大小，也可以理解为标定板离摄像头的远近。

skew：标定板在视野中的倾斜位置。

因此，需要移动标定板：x代表左右移动，y代表上下移动，size代表远近移动，skew代表倾斜侧角，可以上下倾，也可以左右倾。

1、通过不停的移动标定板：直到X、Y、Size、Skew四个都变成绿色

2、当四个都变绿色时，下面的三个按钮也会变成青色，此时点击CALIBRATE开始标定，过程大约1,2分钟

当开始标定后，可以在终端中看到看到标定的信息：

(base) shl@zhihui-mint:/opt/ros/melodic/lib/camera_calibration$ rosrun camera_calibration cameracalibrator.py --size 8x6 --square 0.108 image:=/usb_cam/image_raw camera:=/usb_cam
Waiting for service /usb_cam/set_camera_info ...
OK
*** Added sample 1, p_x = 0.616, p_y = 0.258, p_size = 0.293, skew = 0.201
*** Added sample 2, p_x = 0.379, p_y = 0.302, p_size = 0.345, skew = 0.409
*** Added sample 3, p_x = 0.301, p_y = 0.211, p_size = 0.289, skew = 0.047
*** Added sample 4, p_x = 0.405, p_y = 0.689, p_size = 0.457, skew = 0.172
*** Added sample 5, p_x = 0.306, p_y = 0.278, p_size = 0.384, skew = 0.087
*** Added sample 6, p_x = 0.373, p_y = 0.193, p_size = 0.386, skew = 0.028
*** Added sample 7, p_x = 0.486, p_y = 0.468, p_size = 0.337, skew = 0.117
*** Added sample 8, p_x = 0.619, p_y = 0.661, p_size = 0.344, skew = 0.163
*** Added sample 9, p_x = 0.527, p_y = 0.829, p_size = 0.288, skew = 0.472
*** Added sample 10, p_x = 0.530, p_y = 0.827, p_size = 0.271, skew = 0.289
*** Added sample 11, p_x = 0.816, p_y = 0.619, p_size = 0.312, skew = 0.014
*** Added sample 12, p_x = 0.579, p_y = 0.823, p_size = 0.288, skew = 0.032
*** Added sample 13, p_x = 0.602, p_y = 0.901, p_size = 0.293, skew = 0.219
*** Added sample 14, p_x = 0.511, p_y = 0.878, p_size = 0.320, skew = 0.156
*** Added sample 15, p_x = 0.413, p_y = 0.825, p_size = 0.324, skew = 0.106
*** Added sample 16, p_x = 0.427, p_y = 0.647, p_size = 0.337, skew = 0.148
*** Added sample 17, p_x = 0.664, p_y = 0.346, p_size = 0.313, skew = 0.082
*** Added sample 18, p_x = 0.529, p_y = 0.265, p_size = 0.331, skew = 0.065
*** Added sample 19, p_x = 0.563, p_y = 0.261, p_size = 0.330, skew = 0.485
*** Added sample 20, p_x = 0.498, p_y = 0.302, p_size = 0.295, skew = 0.297
*** Added sample 21, p_x = 0.522, p_y = 0.134, p_size = 0.286, skew = 0.305
*** Added sample 22, p_x = 0.586, p_y = 0.396, p_size = 0.348, skew = 0.005
*** Added sample 23, p_x = 0.444, p_y = 0.177, p_size = 0.324, skew = 0.243
*** Added sample 24, p_x = 0.377, p_y = 0.311, p_size = 0.327, skew = 0.126
*** Added sample 25, p_x = 0.171, p_y = 0.272, p_size = 0.314, skew = 0.101
*** Added sample 26, p_x = 0.287, p_y = 0.069, p_size = 0.314, skew = 0.128
*** Added sample 27, p_x = 0.109, p_y = 0.033, p_size = 0.313, skew = 0.165
*** Added sample 28, p_x = 0.455, p_y = 0.363, p_size = 0.284, skew = 0.025
*** Added sample 29, p_x = 0.409, p_y = 0.061, p_size = 0.310, skew = 0.025
*** Added sample 30, p_x = 0.441, p_y = 0.347, p_size = 0.245, skew = 0.162
*** Added sample 31, p_x = 0.382, p_y = 0.181, p_size = 0.277, skew = 0.148
*** Added sample 32, p_x = 0.330, p_y = 0.203, p_size = 0.251, skew = 0.315
*** Added sample 33, p_x = 0.274, p_y = 0.137, p_size = 0.277, skew = 0.381
*** Added sample 34, p_x = 0.335, p_y = 0.045, p_size = 0.271, skew = 0.236
*** Added sample 35, p_x = 0.645, p_y = 0.609, p_size = 0.189, skew = 0.078
*** Added sample 36, p_x = 0.262, p_y = 0.688, p_size = 0.215, skew = 0.219
*** Added sample 37, p_x = 0.324, p_y = 0.622, p_size = 0.235, skew = 0.150
*** Added sample 38, p_x = 0.258, p_y = 0.556, p_size = 0.244, skew = 0.057
*** Added sample 39, p_x = 0.253, p_y = 0.436, p_size = 0.233, skew = 0.136
*** Added sample 40, p_x = 0.288, p_y = 0.362, p_size = 0.224, skew = 0.232
*** Added sample 41, p_x = 0.264, p_y = 0.364, p_size = 0.235, skew = 0.014
*** Added sample 42, p_x = 0.191, p_y = 0.531, p_size = 0.242, skew = 0.195
*** Added sample 43, p_x = 0.016, p_y = 0.543, p_size = 0.164, skew = 0.076
*** Added sample 44, p_x = 0.078, p_y = 0.369, p_size = 0.172, skew = 0.003
*** Added sample 45, p_x = 0.126, p_y = 0.410, p_size = 0.177, skew = 0.144
*** Added sample 46, p_x = 0.197, p_y = 0.429, p_size = 0.178, skew = 0.349
*** Added sample 47, p_x = 0.424, p_y = 0.090, p_size = 0.197, skew = 0.202
*** Added sample 48, p_x = 0.289, p_y = 0.905, p_size = 0.258, skew = 0.314
*** Added sample 49, p_x = 0.370, p_y = 0.862, p_size = 0.237, skew = 0.240
*** Added sample 50, p_x = 0.030, p_y = 0.835, p_size = 0.237, skew = 0.344
*** Added sample 51, p_x = 0.899, p_y = 0.230, p_size = 0.257, skew = 0.138
*** Added sample 52, p_x = 0.550, p_y = 0.498, p_size = 0.184, skew = 0.006
*** Added sample 53, p_x = 0.481, p_y = 0.548, p_size = 0.234, skew = 0.037
*** Added sample 54, p_x = 0.328, p_y = 0.740, p_size = 0.167, skew = 0.520
*** Added sample 55, p_x = 0.350, p_y = 0.697, p_size = 0.181, skew = 0.366
*** Added sample 56, p_x = 0.379, p_y = 0.735, p_size = 0.176, skew = 0.796
*** Added sample 57, p_x = 0.243, p_y = 0.747, p_size = 0.154, skew = 0.347
*** Added sample 58, p_x = 0.333, p_y = 0.489, p_size = 0.155, skew = 0.150
**** Calibrating ****
D = [-0.3499780364596218, 0.10509003071685562, 0.001679044863502849, 0.008650165715176933, 0.0]
K = [429.8550308646715, 0.0, 302.68475082388386, 0.0, 428.69315916472806, 233.4834792745656, 0.0, 0.0, 1.0]
R = [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0]
P = [329.21832275390625, 0.0, 308.72734114651394, 0.0, 0.0, 374.89495849609375, 232.52836954700615, 0.0, 0.0, 0.0, 1.0, 0.0]
None
# oST version 5.0 parameters


[image]

width
640

height
480

[narrow_stereo]

camera matrix
429.855031 0.000000 302.684751
0.000000 428.693159 233.483479
0.000000 0.000000 1.000000

distortion
-0.349978 0.105090 0.001679 0.008650 0.000000

rectification
1.000000 0.000000 0.000000
0.000000 1.000000 0.000000
0.000000 0.000000 1.000000

projection
329.218323 0.000000 308.727341 0.000000
0.000000 374.894958 232.528370 0.000000
0.000000 0.000000 1.000000 0.000000

('Wrote calibration data to', '/tmp/calibrationdata.tar.gz')
('Wrote calibration data to', '/tmp/calibrationdata.tar.gz')
D = [-0.3499780364596218, 0.10509003071685562, 0.001679044863502849, 0.008650165715176933, 0.0]
K = [429.8550308646715, 0.0, 302.68475082388386, 0.0, 428.69315916472806, 233.4834792745656, 0.0, 0.0, 1.0]
R = [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0]
P = [329.21832275390625, 0.0, 308.72734114651394, 0.0, 0.0, 374.89495849609375, 232.52836954700615, 0.0, 0.0, 0.0, 1.0, 0.0]
# oST version 5.0 parameters


[image]

width
640

height
480

[narrow_stereo]

camera matrix
429.855031 0.000000 302.684751
0.000000 428.693159 233.483479
0.000000 0.000000 1.000000

distortion
-0.349978 0.105090 0.001679 0.008650 0.000000

rectification
1.000000 0.000000 0.000000
0.000000 1.000000 0.000000
0.000000 0.000000 1.000000

projection
329.218323 0.000000 308.727341 0.000000
0.000000 374.894958 232.528370 0.000000
0.000000 0.000000 1.000000 0.000000

(base) shl@zhihui-mint:/opt/ros/melodic/lib/camera_calibration$

3、然后点击Save按钮，会把标定的文件信息保存到：/tmp/calibrationdata.tar.gz路径下

calibrationdata.tar.gz压缩文件中，会存储标定过程中的图片，还有就是标定的参数相机参数文件，如下：

ost.txt文件内容：

# oST version 5.0 parameters


[image]

width
640

height
480

[narrow_stereo]

camera matrix
429.855031 0.000000 302.684751
0.000000 428.693159 233.483479
0.000000 0.000000 1.000000

distortion
-0.349978 0.105090 0.001679 0.008650 0.000000

rectification
1.000000 0.000000 0.000000
0.000000 1.000000 0.000000
0.000000 0.000000 1.000000

projection
329.218323 0.000000 308.727341 0.000000
0.000000 374.894958 232.528370 0.000000
0.000000 0.000000 1.000000 0.000000

ost.yaml文件内容：

image_width: 640
image_height: 480
camera_name: narrow_stereo
camera_matrix:
  rows: 3
  cols: 3
  data: [ 429.85503,    0.     ,  302.68475,
            0.     ,  428.69316,  233.48348,
            0.     ,    0.     ,    1.     ]
camera_model: plumb_bob
distortion_coefficients:
  rows: 1
  cols: 5
  data: [-0.349978, 0.105090, 0.001679, 0.008650, 0.000000]
rectification_matrix:
  rows: 3
  cols: 3
  data: [ 1.,  0.,  0.,
          0.,  1.,  0.,
          0.,  0.,  1.]
projection_matrix:
  rows: 3
  cols: 4
  data: [ 329.21832,    0.     ,  308.72734,    0.     ,
            0.     ,  374.89496,  232.52837,    0.     ,
            0.     ,    0.     ,    1.     ,    0.     ]

参考：blog.csdn.net/learning_to… # 相机和雷达的联合标定参考：blog.csdn.net/learning_to… # ros对相机标定，同时包含标定的理论知识，还有就是去图像畸变的实现参考：blog.csdn.net/qq_41746268… # ros标定参考参考：wiki.ros.org/camera_cali… # ros官网给出的相机标定教程参考：zhuanlan.zhihu.com/p/24651968 # 相机标定的理论知识，很详细参考：wiki.ros.org/camera_cali… # ros官方单目相机的标定教程参考：blog.csdn.net/sinat_16643… # 关于标定参数的解释

今天的文章使用ros自带的标定工具标定相机的内参和外参分享到此就结束了，感谢您的阅读。

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使用ros自带的标定工具标定相机的内参和外参