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#include <sensor_msgs/point_cloud_conversion.h> sensor_msgs::PointCloud2 point_cloud2; sensor_msgs::PointCloud point_cloud; sensor_msgs::convertPointCloudToPointCloud2(point_cloud, point_cloud2); sensor_msgs::convertPointCloud2ToPointCloud(point_cloud2, point_cloud); |
Converting sensor_msgs::PCLPointCloud2 to sensor_msgs::PointCloud and reverse Read More »
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#include <pcl/conversions.h> |
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pcl::PCLPointCloud2 point_cloud2; pcl::PointCloud<pcl::PointXYZ> point_cloud; pcl::fromPCLPointCloud2( point_cloud2, point_cloud); pcl::toPCLPointCloud2(point_cloud, point_cloud2); |
more about pcl point cloud and conversion
Converting pcl::PCLPointCloud2 to pcl::PointCloud and reverse Read More »
For this work, first I loaded the RRBot in Gazebo and launched Its joints controller, then I sent a periodic signal to the robot such that the laser scanner mounted on the robot swings. In the following, I assembled the incoming laser scans with the transformation from tf and created PCL point cloud. Install the necessary
Assembling Laser scans into PCL point cloud Using Gazebo and ROS Read More »
Have you ever wondered why the selfie that you are taking with your phone is so bad while when you taking a photo a decent Nikon or Canon DSLR you get a nice portrait? This happens due to the size of the sensor and focal length. Since the sensor in your cell-phone is very small
Effect of focal length on image Read More »
In my previous tutorial I showed you how to calibrate a camera with ROS. In this tutorial, I’m gonna show you how to rectify the images from coming from your camera. First, open your text editor and paste the following text into it:
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<!-- Example of run: roslaunch usb_cam_stream_publisher.launch video_device:=/dev/video0 image_width:=640 image_height:=480 --> <launch> <arg name="video_device" default="/dev/video0" /> <arg name="image_width" default="640" /> <arg name="image_height" default="480" /> <arg name="image_topic_name" default="/usb_cam/image_raw" /> <arg name="camera_info_topic_name" default="/usb_cam/camera_info" /> <node name="usb_cam" pkg="usb_cam" type="usb_cam_node" output="screen" > <param name="video_device" value="$(arg video_device)" /> <param name="image_width" value="$(arg image_width)" /> <param name="image_height" value="$(arg image_height)"/> <param name="pixel_format" value="mjpeg" /> <param name="io_method" value="mmap"/> <!-- update these line accroding to your setting, here are some examples:--> <!-- <param name="camera_info_url" value="package://your_cameras/info/camera.yaml"/> --> <!-- <param name="camera_info_url" value="file:///home/behnam/.ros/camera_info/head_camera.yaml"/> --> <param name="camera_info_url" value="file://${ROS_HOME}/camera_info/head_camera.yaml"/> <remap from="/usb_cam/camera_info" to="$(arg camera_info_topic_name)"/> <remap from="/usb_cam/image_raw" to="$(arg image_topic_name)"/> </node> </launch> |
make the necessary changes and save it under usb_cam_stream_publisher.launch Now run it:
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roslaunch usb_cam_stream_publisher.launch |
Open
How to use a calibrated camera in ROS Read More »
Real-time object recognition and 6DOF pose estimation with PCL pointcloud and ROS. This approach is based on normal coherence
Real time object recognition and 6DOF pose estimation with PCL pointcloud and ROS Read More »
In this tutorial, I’m gonna show you how to do object recognition and 6DOF pose estimation in real-time based on Linemod algorithm with ROS and PCL pointcloud. First, you need to install ork:
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sudo apt-get install ros-indigo-object-recognition-ros-visualization ros-indigo-object-recognition-msgs ros-indigo-object-recognition-core |
Then add the model of your object for tracking to Couch DB:
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rosrun object_recognition_core object_add.py -n coke -d "A universal can of coke" --commit |
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rosrun object_recognition_core mesh_add.py <YOUR_OBJECT_ID_FROM_PREVIOUS_STEP> /home/behnam/ork_tutorials/data/coke.obj --commit |
You need to install Kinect driver, if you don’t
This Matlab tutorial I use SIFT, RANSAC, and homography to find corresponding points between two images. Here I have used vlfeat to find SIFT features. Full code is available at my GitHub repository Major steps are: 0.Adding vlfeat to your Matlab workspace:
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run('<path_to_vlfeat>/toolbox/vl_setup') |
1.Detect key points and extract descriptors. In the image below you can see some SIFT key
Stitching image using SIFT and Homography Read More »
In many applications, you need to track an object. One simple method is color based tracking. I have developed a simple tool for that with OpenCV. All you have to do is just to adjust the High and Low values of HSV slider in the left window till you filter the image and you only see your
Colour based object Tracking with OpenCV Read More »
