This work is the second part of my master thesis (part I). In this part, I developed an algorithm for 2D pose estimation of the human body. To do this, I created a software with QT that could generate 2D contours representing human body. Then I send these contours for evaluation to CNS(Contrast Normalized Sobel) [1] […]
2D pose estimation of human body using CNS and PCA 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:
|
1 |
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:
|
1 |
rosrun object_recognition_core object_add.py -n coke -d "A universal can of coke" --commit |
|
1 |
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
ArUco is a simple yet great library for augmented reality applications. In this tutorial, I’m gonna show you how to track ArUco marker and estimate their 6DOF pose with ROS. For this tutorial, you only need a USB camera. You need to calibrate your camera before first. If you don’ know how to that just follow my other tutorial
6DOF pose estimation with Aruco marker and ROS Read More »
In this work at first, I recognize the object in the scene and estimate the 6 DOF pose of that. Then I track the object by using particle filter. RGB data acquired from Kinect 2 and turned into PCL pointcloud. I demonstrate a task several times to the robot. In this case, I move an object (a detergent) over
Learning From Demonstration Read More »
