ROS Archives - Robotics with ROS

How to use image_geometry and camera_info_manager in ROS

Leave a Comment / C++, Computer Vision, ROS, Tutorials / By admin

camera_info_publisher.cpp:

#include <camera_info_manager/camera_info_manager.h>
int main(int argc, char** argv)
{
    ros::init(argc, argv, "camera_info_publisher");
    ros::NodeHandle nh;

    const std::string cname="prosilica";
    const std::string url="file://${ROS_HOME}/camera_info/prosilica.yaml";
    ros::Publisher pubCameraInfo = nh.advertise<sensor_msgs::CameraInfo>("camera/camera_info", 1);
    camera_info_manager::CameraInfoManager cinfo(nh,cname,url);
    sensor_msgs::CameraInfo msgCameraInfo;
    ros::Rate loop_rate(5);
    while (nh.ok())
    {
        msgCameraInfo=cinfo.getCameraInfo();
        pubCameraInfo.publish(msgCameraInfo);
        ros::spinOnce();
        loop_rate.sleep();
    }
}

#include <camera_info_manager/camera_info_manager.h>

int main(int argc, char** argv)

{

ros::init(argc, argv, "camera_info_publisher");

ros::NodeHandle nh;

const std::string cname="prosilica";

const std::string url="file://${ROS_HOME}/camera_info/prosilica.yaml";

ros::Publisher pubCameraInfo = nh.advertise<sensor_msgs::CameraInfo>("camera/camera_info", 1);

camera_info_manager::CameraInfoManager cinfo(nh,cname,url);

sensor_msgs::CameraInfo msgCameraInfo;

ros::Rate loop_rate(5);

while (nh.ok())

{

msgCameraInfo=cinfo.getCameraInfo();

pubCameraInfo.publish(msgCameraInfo);

ros::spinOnce();

loop_rate.sleep();

}

image_geometry_demo.cpp:

#include <ros/ros.h>
#include <image_geometry/pinhole_camera_model.h>

void cameraInfoCallback(const sensor_msgs::CameraInfoConstPtr& info_msg)
{
    std::cout<<"===============================cameraInfoCallback===============================" <<std::endl;
    image_geometry::PinholeCameraModel cam_model_;
    cam_model_.fromCameraInfo(info_msg);
    // projection_matrix is the matrix you should use if you don't want to use project3dToPixel() and want to use opencv API
    cv::Matx34d projection_matrix=cam_model_.fullProjectionMatrix();
    std::cout<<cam_model_.project3dToPixel(cv::Point3d(-0.1392072,-0.02571392, 2.50376511) )<<std::endl;
}

int main(int argc,char ** argv)
{
    ros::init(argc,argv,"image_geometry_demo",1);
    ros::NodeHandle nh;
    ros::Subscriber sub = nh.subscribe("camera/camera_info", 1000, cameraInfoCallback);
    ros::spin();
}

#include <ros/ros.h>

#include <image_geometry/pinhole_camera_model.h>

void cameraInfoCallback(const sensor_msgs::CameraInfoConstPtr& info_msg)

{

std::cout<<"===============================cameraInfoCallback===============================" <<std::endl;

image_geometry::PinholeCameraModel cam_model_;

cam_model_.fromCameraInfo(info_msg);

// projection_matrix is the matrix you should use if you don't want to use project3dToPixel() and want to use opencv API

cv::Matx34d projection_matrix=cam_model_.fullProjectionMatrix();

std::cout<<cam_model_.project3dToPixel(cv::Point3d(-0.1392072,-0.02571392, 2.50376511) )<<std::endl;

}

int main(int argc,char ** argv)

{

ros::init(argc,argv,"image_geometry_demo",1);

ros::NodeHandle nh;

ros::Subscriber sub = nh.subscribe("camera/camera_info", 1000, cameraInfoCallback);

ros::spin();

}

CMakeLists.txt

find_package(catkin REQUIRED COMPONENTS image_geometry camera_info_manager roscpp)

1	find_package(catkin REQUIRED COMPONENTS image_geometry camera_info_manager roscpp)

RGBD PCL point cloud from Stereo vision with ROS and OpenCV

Leave a Comment / Computer Vision, Image Processing, ROS, Tutorials / By admin

In my other tutorial, I showed you how to calibrate you stereo camera. After Calibration, we can get disparity map and RGBD PCL point cloud from our stereo camera cool huh 🙂 1)Save the following text under “stereo_usb_cam_stream_publisher.launch”

<launch>

<arg name="video_device_right" default="/dev/video1" />
<arg name="video_device_left" default="/dev/video2" />

<arg name="image_width" default="640" />
<arg name="image_height" default="480" />

<arg name="right_image_namespace" default="stereo" />
<arg name="left_image_namespace" default="stereo" />

<arg name="right_image_node" default="right" />
<arg name="left_image_node" default="left" />

<arg name="image_topic_name_right" default="image_raw" />
<arg name="camera_info_topic_name_right" default="camera_info" />

<arg name="image_topic_name_left" default="image_raw" />
<arg name="camera_info_topic_name_left" default="camera_info" />



<arg name="left_camera_name" default="left" />
<arg name="right_camera_name" default="right" />

<arg name="left_camera_frame_id" default="left_camera" />
<arg name="right_camera_frame_id" default="right_camera" />

<arg name="framerate" default="30" />

<arg name="camera_info" default="false" />
<arg name="camera_info_path" default="stereo_camera_info" />
	  
<node ns="$(arg right_image_namespace)" name="$(arg right_image_node)" pkg="usb_cam" type="usb_cam_node" output="screen" >
	<param name="video_device" value="$(arg video_device_right)" />
	<param name="image_width" value="$(arg image_width)" />
	<param name="image_height" value="$(arg image_height)"/>
	<param name="pixel_format" value="yuyv" />
	<param name="io_method" value="mmap"/>
	<remap from="/usb_cam/image_raw" to="$(arg image_topic_name_right)" />
	<param name="framerate" value="$(arg framerate)"/> 
	<!-- if camera_info is available, we will use it-->
	<param name="camera_frame_id" value="$(arg right_camera_frame_id)" if="$(arg camera_info)" /> 
	<param name="camera_info_url" value="file://${ROS_HOME}/$(arg camera_info_path)/right.yaml" if="$(arg camera_info)"/>
	<param name="camera_name" value="narrow_stereo/$(arg right_camera_name)" if="$(arg camera_info)"/> 
	<remap from="/usb_cam/camera_info" to="$(arg camera_info_topic_name_right)" if="$(arg camera_info)"/>

</node>

<node ns="$(arg left_image_namespace)" name="$(arg left_image_node)" pkg="usb_cam" type="usb_cam_node" output="screen" >
	<param name="video_device" value="$(arg video_device_left)" />
	<param name="image_width" value="$(arg image_width)" />
	<param name="image_height" value="$(arg image_height)"/>
	<param name="pixel_format" value="yuyv" />
	<param name="io_method" value="mmap"/>
	<param name="framerate" value="$(arg framerate)"/> 
	<remap from="/usb_cam/image_raw" to="$(arg image_topic_name_left)"/>
	<!-- if camera_info is available, we will use it-->
	<param name="camera_frame_id" value="$(arg left_camera_frame_id)" if="$(arg camera_info)"/> 
	<param name="camera_name" value="narrow_stereo/$(arg left_camera_name)" if="$(arg camera_info)"/> 
	<param name="camera_info_url" value="file://${ROS_HOME}/$(arg camera_info_path)/left.yaml" if="$(arg camera_info)"/> 
	<remap from="/usb_cam/camera_info" to="$(arg camera_info_topic_name_left)" if="$(arg camera_info)"/>
</node>
</launch>

</node>

</node>

</launch>

2) Then run the following node to publish both cameras and camera info (calibration matrix)

roslaunch stereo_usb_cam_stream_publisher.launch video_device_right:=/dev/video1 video_device_left:=/dev/video2 image_width:=640 image_height:=480 left_camera_name:=left right_camera_name:=right camera_info:=true camera_info_path:=stereo_camera_info

1	roslaunch stereo_usb_cam_stream_publisher.launch video_device_right:=/dev/video1 video_device_left:=/dev/video2 image_width:=640 image_height:=480 left_camera_name:=left right_camera_name:=right camera_info:=true camera_info_path:=stereo_camera_info

3) Run the …

RGBD PCL point cloud from Stereo vision with ROS and OpenCV Read More »

Stereo Camera Calibration with ROS and OpenCV

5 Comments / Computer Vision, Image Processing, ROS, Tutorials / By admin

In this tutorial, I’m gonna show you stereo camera calibration with ROS and OpenCV. So you need a pair of cameras, I bought a pair of this USB webcam which is okay for this task. 1)Save the following text under “stereo_usb_cam_stream_publisher.launch”

<launch>

<arg name="video_device_right" default="/dev/video1" />
<arg name="video_device_left" default="/dev/video2" />

<arg name="image_width" default="640" />
<arg name="image_height" default="480" />

<arg name="right_image_namespace" default="stereo" />
<arg name="left_image_namespace" default="stereo" />

<arg name="right_image_node" default="right" />
<arg name="left_image_node" default="left" />

<arg name="image_topic_name_right" default="image_raw" />
<arg name="camera_info_topic_name_right" default="camera_info" />

<arg name="image_topic_name_left" default="image_raw" />
<arg name="camera_info_topic_name_left" default="camera_info" />



<arg name="left_camera_name" default="left" />
<arg name="right_camera_name" default="right" />

<arg name="left_camera_frame_id" default="left_camera" />
<arg name="right_camera_frame_id" default="right_camera" />

<arg name="framerate" default="30" />

<arg name="camera_info" default="false" />
<arg name="camera_info_path" default="stereo_camera_info" />
	  
<node ns="$(arg right_image_namespace)" name="$(arg right_image_node)" pkg="usb_cam" type="usb_cam_node" output="screen" >
	<param name="video_device" value="$(arg video_device_right)" />
	<param name="image_width" value="$(arg image_width)" />
	<param name="image_height" value="$(arg image_height)"/>
	<param name="pixel_format" value="yuyv" />
	<param name="io_method" value="mmap"/>
	<remap from="/usb_cam/image_raw" to="$(arg image_topic_name_right)" />
	<param name="framerate" value="$(arg framerate)"/> 
	<!-- if camera_info is available, we will use it-->
	<param name="camera_frame_id" value="$(arg right_camera_frame_id)" if="$(arg camera_info)" /> 
	<param name="camera_info_url" value="file://${ROS_HOME}/$(arg camera_info_path)/right.yaml" if="$(arg camera_info)"/>
	<param name="camera_name" value="narrow_stereo/$(arg right_camera_name)" if="$(arg camera_info)"/> 
	<remap from="/usb_cam/camera_info" to="$(arg camera_info_topic_name_right)" if="$(arg camera_info)"/>

</node>

<node ns="$(arg left_image_namespace)" name="$(arg left_image_node)" pkg="usb_cam" type="usb_cam_node" output="screen" >
	<param name="video_device" value="$(arg video_device_left)" />
	<param name="image_width" value="$(arg image_width)" />
	<param name="image_height" value="$(arg image_height)"/>
	<param name="pixel_format" value="yuyv" />
	<param name="io_method" value="mmap"/>
	<param name="framerate" value="$(arg framerate)"/> 
	<remap from="/usb_cam/image_raw" to="$(arg image_topic_name_left)"/>
	<!-- if camera_info is available, we will use it-->
	<param name="camera_frame_id" value="$(arg left_camera_frame_id)" if="$(arg camera_info)"/> 
	<param name="camera_name" value="narrow_stereo/$(arg left_camera_name)" if="$(arg camera_info)"/> 
	<param name="camera_info_url" value="file://${ROS_HOME}/$(arg camera_info_path)/left.yaml" if="$(arg camera_info)"/> 
	<remap from="/usb_cam/camera_info" to="$(arg camera_info_topic_name_left)" if="$(arg camera_info)"/>
</node>
</launch>

</node>

</node>

</launch>

2)Then run the following node to publish both cameras.

roslaunch stereo_usb_cam_stream_publisher.launch video_device_right:=/dev/video1 video_device_left:=/dev/video2 image_width:=640 image_height:=480 left_camera_name:=left right_camera_name:=right

1	roslaunch stereo_usb_cam_stream_publisher.launch video_device_right:=/dev/video1 video_device_left:=/dev/video2 image_width:=640 image_height:=480 left_camera_name:=left right_camera_name:=right

3)Now call the calibration node: …

Stereo Camera Calibration with ROS and OpenCV Read More »

Connecting PS4 Controller dualshock via Bluetooth in Ubuntu

9 Comments / ROS, Tutorials / By admin

PS4 controllers work out of the box in Ubuntu with USB cable but I was looking for a way to get it work via Bluetooth as well. After installing a couple of packages I found “ds4drv”. To install it:

sudo pip install ds4drv

1	sudo pip install ds4drv

Then you have to either add the user to the list of root user or simply run …

Connecting PS4 Controller dualshock via Bluetooth in Ubuntu Read More »

A GUI ROS-package for cropping pcl pointcloud with dynamic reconfigure

2 Comments / C++, Computer Vision, Image Processing, ROS, Tutorials / By admin

This ROS package enables you to crop the scene from Kinect (input topic type: PCL pointcloud). You can even enable fitting a plane to remove the ground from the scene and by adjusting correct parameter you can get the desired object from the scene. code available on my Github.

How to create octomap tree from mesh

2 Comments / C++, ROS, Tutorials / By admin

I was looking for a way to create an octomap tree from arbitrary mesh file. Well at first I tried to convert my data into PCL pointcloud and then convert them into an octomap tree. But the problem was, for instance when you a cuboid mesh, you only have 8 vertex, which gives you 8 point and the walls between …

How to create octomap tree from mesh Read More »

Octomap explanierend

Leave a Comment / Machine Learning, ROS, Tutorials / By admin

In this tutorial, I explain the concept, probabilistic sensor fusion model and the sensor model used in Octomap library. related publication: OctoMap: An Efficient Probabilistic 3D Mapping Framework Based on Octrees 1)Octamap Volumetric Model 2)Probabilistic Sensor Fusion Model 3)Sensor Model for Laser Range Data

Finding roll, pitch yaw from 3X3 rotation matrix with Eigen

Leave a Comment / C++, ROS, Tutorials / By admin

/*  http://planning.cs.uiuc.edu/node103.html

    |r11 r12 r13 |
    |r21 r22 r23 |
    |r31 r32 r33 |

    yaw: alpha=arctan(r21/r11)
    pitch: beta=arctan(-r31/sqrt( r32^2+r33^2 ) )
    roll: gamma=arctan(r32/r33)
*/
    double roll, pitch, yaw;
    roll=M_PI/3;
    pitch=M_PI/4;
    yaw=M_PI/6;
    Eigen::AngleAxisd rollAngle(roll, Eigen::Vector3d::UnitX());
    Eigen::AngleAxisd pitchAngle(pitch, Eigen::Vector3d::UnitY());
    Eigen::AngleAxisd yawAngle(yaw, Eigen::Vector3d::UnitZ());

    Eigen::Quaternion<double> q =  yawAngle*pitchAngle *rollAngle;
    Eigen::Matrix3d rotationMatrix = q.matrix();



    std::cout<<"roll is Pi/" <<M_PI/atan2( rotationMatrix(2,1),rotationMatrix(2,2) ) <<std::endl;
    std::cout<<"pitch: Pi/" <<M_PI/atan2( -rotationMatrix(2,0), std::pow( rotationMatrix(2,1)*rotationMatrix(2,1) +rotationMatrix(2,2)*rotationMatrix(2,2) ,0.5  )  ) <<std::endl;
    std::cout<<"yaw is Pi/" <<M_PI/atan2( rotationMatrix(1,0),rotationMatrix(0,0) ) <<std::endl;

/* http://planning.cs.uiuc.edu/node103.html

|r11 r12 r13 |

|r21 r22 r23 |

|r31 r32 r33 |

yaw: alpha=arctan(r21/r11)

pitch: beta=arctan(-r31/sqrt( r32^2+r33^2 ) )

roll: gamma=arctan(r32/r33)

double roll, pitch, yaw;

roll=M_PI/3;

pitch=M_PI/4;

yaw=M_PI/6;

Eigen::AngleAxisd rollAngle(roll, Eigen::Vector3d::UnitX());

Eigen::AngleAxisd pitchAngle(pitch, Eigen::Vector3d::UnitY());

Eigen::AngleAxisd yawAngle(yaw, Eigen::Vector3d::UnitZ());

Eigen::Quaternion<double> q = yawAngle*pitchAngle *rollAngle;

Eigen::Matrix3d rotationMatrix = q.matrix();

std::cout<<"roll is Pi/" <<M_PI/atan2( rotationMatrix(2,1),rotationMatrix(2,2) ) <<std::endl;

std::cout<<"pitch: Pi/" <<M_PI/atan2( -rotationMatrix(2,0), std::pow( rotationMatrix(2,1)*rotationMatrix(2,1) +rotationMatrix(2,2)*rotationMatrix(2,2) ,0.5 ) ) <<std::endl;

std::cout<<"yaw is Pi/" <<M_PI/atan2( rotationMatrix(1,0),rotationMatrix(0,0) ) <<std::endl;

Roll, pitch, yaw using Eigen and KDL Frame

Leave a Comment / C++, Linear Algebra, ROS, Tutorials / By admin

/*
yaw:
    A yaw is a counterclockwise rotation of alpha about the  z-axis. The rotation matrix is given by

    R_z

    |cos(alpha) -sin(alpha) 0|
    |sin(apha)   cos(alpha) 0|
    |    0            0     1|

pitch:
    R_y
    A pitch is a counterclockwise rotation of  beta about the  y-axis. The rotation matrix is given by

    |cos(beta)  0   sin(beta)|
    |0          1       0    |
    |-sin(beta) 0   cos(beta)|

roll:
    A roll is a counterclockwise rotation of  gamma about the  x-axis. The rotation matrix is given by
    R_x
    |1          0           0|
    |0 cos(gamma) -sin(gamma)|
    |0 sin(gamma)  cos(gamma)|



    It is important to note that   R_z R_y R_x performs the roll first, then the pitch, and finally the yaw
    Roration matrix: R_z*R_y*R_x
*/

    double roll, pitch, yaw;
    roll=M_PI/3;
    pitch=M_PI/4;
    yaw=M_PI/6;
    Eigen::AngleAxisd rollAngle(roll, Eigen::Vector3d::UnitX());
    Eigen::AngleAxisd pitchAngle(pitch, Eigen::Vector3d::UnitY());
    Eigen::AngleAxisd yawAngle(yaw, Eigen::Vector3d::UnitZ());




    Eigen::Quaternion<double> q =  yawAngle*pitchAngle *rollAngle;

    Eigen::Matrix3d rotationMatrix = q.matrix();
    std::cout<<rotationMatrix <<std::endl;

yaw:

A yaw is a counterclockwise rotation of alpha about the z-axis. The rotation matrix is given by

R_z

|cos(alpha) -sin(alpha) 0|

|sin(apha) cos(alpha) 0|

| 0 0 1|

pitch:

R_y

A pitch is a counterclockwise rotation of beta about the y-axis. The rotation matrix is given by

|cos(beta) 0 sin(beta)|

|0 1 0 |

|-sin(beta) 0 cos(beta)|

roll:

A roll is a counterclockwise rotation of gamma about the x-axis. The rotation matrix is given by

R_x

|1 0 0|

|0 cos(gamma) -sin(gamma)|

|0 sin(gamma) cos(gamma)|

It is important to note that R_z R_y R_x performs the roll first, then the pitch, and finally the yaw

Roration matrix: R_z*R_y*R_x

double roll, pitch, yaw;

roll=M_PI/3;

pitch=M_PI/4;

yaw=M_PI/6;

Eigen::AngleAxisd rollAngle(roll, Eigen::Vector3d::UnitX());

Eigen::AngleAxisd pitchAngle(pitch, Eigen::Vector3d::UnitY());

Eigen::AngleAxisd yawAngle(yaw, Eigen::Vector3d::UnitZ());

Eigen::Quaternion<double> q = yawAngle*pitchAngle *rollAngle;

Eigen::Matrix3d rotationMatrix = q.matrix();

std::cout<<rotationMatrix <<std::endl;

From Eigen documentation: If you are working with OpenGL 4×4 matrices then Affine3f and Affine3d are what you want. Since Eigen defaults to column-major storage, you can directly use the Transform::data() method to pass your transformation matrix to OpenGL. construct a Transform:

Transform t(AngleAxis(angle,axis));

1	Transform t(AngleAxis(angle,axis));

or like this:

Transform t;
t = AngleAxis(angle,axis);

1 2	Transform t; t = AngleAxis(angle,axis);

But note that unfortunately, because of …

Roll, pitch, yaw using Eigen and KDL Frame Read More »

ROS packages for Dynamic Time Warping

Leave a Comment / C++, Machine Learning, ROS / By admin

Dynamic Time Warping (DTW) is a method to align two sequences under certain constraints. For instance, two trajectories that are very similar but one of them performed in a longer time. The alignment should be is such way that minimizes the distance between these two sequences. Here I have done DTW between two-time series with python. I found a …

ROS packages for Dynamic Time Warping Read More »

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