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Under Construction

The documentation in this file has not been updated to reflect the new code layout. The LabelFusion source code was previously a subdirectory in the Spartan git repository, and the documentation in this file assumes the old layout. Please do not attempt to use this documentation until it has been updated.

Pipeline

1. Collect RGBD data

In first terminal, use_spartan and then launch:

  • kuka_iiwa_procman
  • Ctrl+R on vision-drivers --> openni-driver (unplug-replug if not working)
  • Ctrl+R on bot-spy
  • Verify that OPENNI_FRAME traffic is coming over lcm

In second terminal, use_spartan and then:

  • lcm-logger
  • Ctrl+C when done logging

Your data should now be saved as lcmlog-*

Automated Collection with Kuka Arm

Automated arm setup

  • kuka_iiwa_procman
  • Ctrl+R on vision-drivers --> openni-driver (unplug-replug if not working)
  • double click on kuka-driver and edit command to be: kuka_driver -fri_port 30201 (or different port)
  • start iiwa-drivers group

Same as above but we will use the kuka arm to move the xtion around. Set useLabelFusionDev = True in iiwaManipApp.py. This launches director with dc = DataCollection object constructed.

  • Spawning a table frame. With the measurement panel activated make three clicks in openni point cloud. First on front edge of table, second in middle of table in a direction perpendicular to the front edge, the third should be above the able. Then dc.spawnTableFrame().
  • Next make the target frames for the planner. The relevant parameters are in config/data_collection.yaml. Use dc.makeTargetCameraFrames().
  • Construct a DataCollectionPlanRunner object by using planRunner = dc.makePlanRunner().
  • Start your lcm log.
  • planRunner.start() to start the plan runner. It will make (and commit) plans to hit all the frames.

2. Trim RGBD data and prepare for processing scripts.

In one terminal, open a viewer for the data:

cds && cd apps/iiwa
directorPython -m director.imageviewapp --bot-config $LABELFUSION_SOURCE_DIR/config/bot_frames.cfg --channel OPENNI_FRAME --rgbd --pointcloud

In another terminal, play the log with a GUI. (Replace mylog.lcmlog with name of log):

lcm-logplayer-gui mylog.lcmlog

Use the log player to scroll and find when you want to start and stop your log, then trim it with, for example:

bot-lcm-logfilter -s 2.3 -e 25.2 mylog.lcmlog trimmedlog.lcmlog

Where 2.3 and 25.2 are example start / stop times (in seconds) from the original log.

To prepare this data for processing, put the trimmed log in a folder, such as with the following hierarchy:

path-do-data/logs/
        log-1/
                trimmedlog.lcmlog
                info.yaml

Where info.yaml is a two-line file that has for example this form:

lcmlog: "trimmedlog.lcmlog"
objects: ["phone","robot","toothpaste","oil_bottle"]

The first line identifies the log to be used, and the second identifies which objects are in the scene. (Meshes for these objects should be set up as specified above.)

3. Run RGBD data through ElasticFusion

Navigate to ElasticFusion executable (in ElasticFusion/GUI/build) and then run, for example:

./ElasticFusion -l path-to-data/logs/log-1/trimmedlog.lcmlog  -f

Where path-to-data/logs/log-1/trimmedlog.lcmlog is the full path to RGBD lcm data. Note that -f option flips the blue/green, which is needed.

When ElasticFusion is done running, the two key files it will save are:

  • *.posegraph
  • *.ply

Rename:

mv *.posegraph posegraph.posegraph

4. Convert ElasticFusion .ply output to .vtp

First, open the .ply file in Meshlab, and save it (this will convert to an ASCII .ply file)

Next, convert to .vtp using the command:

directorPython scripts/convertPlyToVtp.py /path/to/data.ply

Now rename:

mv *.vtp reconstructed_pointcloud.vtp

5. Global Object Pose Fitting

The class that handles segmentation and registration is in modules/labelfusion/registration.py and modules/labelfusion/objectalignmenttool.py. Launch the standard labelFusionApp to run it:

directorPython $LABELFUSION_SOURCE_DIR/scripts/labelFusionApp.py --bot-config $LABELFUSION_SOURCE_DIR/config/bot_frames.cfg --logFolder scenes/2017-06-15-70

The GlobalRegistration object is in the global namespace as globalRegistration, or gr for short. The first step is to align the reconstructed point cloud so it is right-side-up:

  • Open measurement panel (View -> Measurement Panel), then check Enabled in measurement panel

  • Use (shift + click) and click two points: first on the surface of the table, then on a point above the table

  • Open Director terminal with F8 and run:

    gr.rotateReconstructionToStandardOrientation()
    
  • Close the labelFusionApp application (ctrl + c) and reopen

The second step is to segment the pointcloud above the table

  • Open measurement panel (View -> Measurement Panel), then check Enabled in measurement panel

  • Use (shift + click) and click two points: first on the surface of the table, then on a point above the table

  • Open Director terminal with F8 and run:

    gr.segmentTable()
    gr.saveAboveTablePolyData()
    
  • Close the labelFusionApp application (ctrl + c) and reopen

Now, we are ready to align each object. Press F8 in the app to open Director's Python terminal and run:

gr.launchObjectAlignment(<objectName>)

where <objectName> is a string like "oil_bottle". This launches a new window. Click the same three points in model and on pointcloud. Using shift + click to do this. After you do this the affordance should appear in main window using the transform that was just computed. You can crop the pointcloud using the alignments we just got by calling:

gr.cropPointCloudUsingAlignedObject(objectName=<objectName>)

Later we will document how to do ICP.

When you are done with an object's registration, run:

gr.saveRegistrationResults()

Issues:

  • red spheres disappear when doing second object alignment

6. Extract Images from LCM log

The class that is used is is modules/labelfusion/imagecapture.py. To extract rgb images from the lcm log run:

directorPython scripts/extractImagesFromLog.py --logFolder logs/moving-camera --bot-config $SPARTAN_SOURCE_DIR/apps/iiwa/iiwaManip.cfg

This will save the images in data/logFolder. The original images will be in the form uid_rbg.png. Each image also has uid_utime.txt which contains the utime associated with that image. Note that it will overwrite anything that is already there.

7. Generate Labeled Images

The class that is used to render labeled images is modules/labelfusion/rendertrainingimages.py. Usage:

directorPython scripts/renderTrainingImages.py --bot-config $SPARTAN_SOURCE_DIR/apps/iiwa/iiwaManip.cfg --logFolder logs/moving-camera

Optionally you can pass --logFolder <logFolder> on the command line where <logFolder> is the path to the lcm log folder relative to the data folder. For example --logFolder logs/moving-camera. This will generate uid_labels.png and uid_color_labels.png which are the labeled images.

Misc

Director with LabelFusion Modules

There is a standalone app for launching a director with labelfusion modules:

directorPython $LABELFUSION_SOURCE_DIR/scripts/labelFusionApp.py --logFolder logs/moving-camera --bot-config $SPARTAN_SOURCE_DIR/apps/iiwa/iiwaManip.cfg

The --logFolder option specifies which logFolder to use relative to LabelFusion data directory.

Visualizing RGBD Data

You can launch director with imageviewapp. You need to pass the --bot-config flag to director along with the config file:

cds && cd apps/iiwa
directorPython -m director.imageviewapp --bot-config $LABELFUSION_SOURCE_DIR/config/bot_frames.cfg --channel OPENNI_FRAME --rgbd --pointcloud