Quick Guide

Instructions are slightly different on whether you use ROS or not. For ROS, instructions will work whether you build ROS from source or not.

From Source From ROS packages

Install

Install ORK as detailed on the Install page.

If you want to visualize data in the database, you also need to run (be sure to have installed couchapp sudo pip install -U couchapp):

cd object_recognition_core/web_ui && ./push.sh

rosrun object_recognition_core push.sh

Setup Your Environment

You need to setup your environment:

source devel/setup.sh

source /opt/ros/DISTRO_NAME/setup.sh

This will add all the necessary packages to your PATH, LD_LIBRARY_PATH and PYTHONPATH.

Setup ROS

Terminal 1:

roscore

Terminal 2:

roslaunch openni_launch openni.launch

Setup the capture workspace

First capture an ORB template of your capture workspace. It should be take from an planar frontal view, and the center of the image should be filled by the plane. Press ‘s’ to save an image. The result will be placed in the directory given, e.g. my_textured_plane. Press ‘q’ to quit the template capture program.

./object_recognition_capture/apps/orb_template -o my_textured_plane

Terminal 3:

rosrun object_recognition_capture orb_template -o my_textured_plane

Try out tracking to see if you got a good template. Press ‘q’ to quit.

rosrun object_recognition_capture orb_track --track_directory my_textured_plane

Uuse the SXGA (roughly 1 megapixel) mode of your openni device if possible.

rosrun dynamic_reconfigure dynparam set /camera/driver image_mode 1
rosrun dynamic_reconfigure dynparam set /camera/driver depth_registration True

Capture objects

Once you are happy with the workspace tracking, its time to capure an object. Place an object at the origin of the workspace. An run the capture program in preview mode. Make sure the mask and pose are being picked up.

./object_recognition_capture/apps/capture -i my_textured_plane --seg_z_min 0.01 -o silk.bag --preview

rosrun object_recognition_capture capture -i my_textured_plane --seg_z_min 0.01 -o silk.bag --preview

When satisified by the preview mode, run it for real. The following will capture a bag of 60 views where each view is normally distributed on the view sphere. The mask and pose displays should only refresh when a novel view is captured. The program will finish when 35 (-n) views are captured. Press ‘q’ to quit early.

./object_recognition_capture/apps/capture -i my_textured_plane --seg_z_min 0.01 -o silk.bag

rosrun object_recognition_capture capture -i my_textured_plane --seg_z_min 0.01 -o silk.bag

Now time for upload. Make sure you install couch db on your machine. Give the object a name and useful tags seperated by a space, e.g. milk soy silk.

./object_recognition_capture/apps/upload -i silk.bag -n 'Silk' milk soy silk --commit

rosrun object_recognition_capture upload -i silk.bag -n 'Silk' milk soy silk --commit

Train objects

Repeat the steps above for the objects you would like to recognize. Once you have captured and uploaded all of the data, it time to mesh and train object recognition.

Meshing objects can be done in a batch mode as follows:

./object_recognition_reconstruction/apps/mesh_object --all --visualize --commit

rosrun object_recognition_reconstruction mesh_object --all --visualize --commit

The currently stored models are on http://localhost:5984/or_web_ui/_design/viewer/meshes.html

Next objects should be trained. It may take some time between objects, this is normal. Also, this quickguide assumes that you are using TOD which only works for textured objects. Please refer to the documentation of other methods.

./object_recognition_core/apps/training \
-c object_recognition_tod/conf/training.ork \
--visualize

rosrun object_recognition_core training \
-c `rospack find object_recognition_tod`/conf/training.ork \
--visualize

Detect objects

Now we’re ready for detection. First launch rviz, it should be subscribed to the right markers for recognition results. /markers is used for the results, and it is a marker array.

./rosrun object_recognition_core/apps/detection \
-c object_recognition_tod/conf/detection.ork \
--visualize

rosrun object_recognition_core detection \
-c `rospack find object_recognition_tod`/conf/detection.ros.ork \
--visualize