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 »
First install the liburdfdom-tools
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sudo apt-get install liburdfdom-tools |
Then
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urdf_to_graphiz <robot_model.urdf> |
How to create hierarchical graph from your robot model (URDF) Read More »
First, install the necessary package:
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sudo apt-get install ros-indigo-joy |
make sure your joystick is working:
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sudo jstest /dev/input/js0 |
also, make sure your controller has correct permissions:
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ls -l /dev/input/js0 |
The permission on the last column must be rw, if it “–” like this:
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crw-rw-r--+ 1 root root 13, 0 May 18 12:12 /dev/input/js0 |
then correct the permission:
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sudo chmod a+rw /dev/input/js0 |
run the Joy node and roscore:
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roscore rosparam set joy_node/dev "/dev/input/js0" rosrun joy joy_node |
The joystick data are being published under
Controlling your robot with joystick and reading data from it 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 »
In this tutorial, I’m gonna show you how to create hybrid images . Basically, you need to filter your first image with high pass filter and the second image with a low pass filter and then add theses two images. The following code shows how to do that. The full code is available at my GitHub.
Creating Hybrid Image with Matlab 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
In this tutorial, we do some SLAM with TurtleBot robot. 1.Before anything, you have to install all packages for gazebo and gmapping and TurtleBot:
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sudo apt-get install ros-kinetic-gmapping ros-kinetic-turtlebot-gazebo ros-kinetic-turtlebot-simulator ros-kinetic-turtlebot-teleop ros-kinetic-turtlebot-rviz-launchers |
2.Launch gazebo and TurtleBot
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roslaunch turtlebot_gazebo turtlebot_world.launch |
3. Call the gmapper to read laser scan and build the map:
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roslaunch turtlebot_gazebo gmapping_demo.launch |
Only for indigo: if you got and error, you need to do some hacky stuff
SLAM using gmapping with TurtleBot robot and Gazbo Read More »
Rapidly-exploring random trees (RRT) and their variant are a very power solution for solving motion planning problem in robotics, but they suffer from finding an optimise solution and the generated path is usually jerky with redundant movements. Sample based-optimisation-based planners benefit the robustness of RRT and the possibility of imposing a cost function. Here in this work, I
Sample based-optimisation-based planner with signed distance fields cost map Read More »
