Generate and export point cloud from Project Tango

Question

After some weeks of waiting I finally have my Project Tango. My idea is to create an app that generates a point cloud of my room and exports this to .xyz data. I'll then use the .xyz file to show the point cloud in a browser! I started off by compiling and adjusting the point cloud example that's on Google's github.

Right now I use the onXyzIjAvailable(TangoXyzIjData tangoXyzIjData) to get a frame of x y and z values; the points. I then save these frames in a PCLManager in the form of Vector3. After I'm done scanning my room, I simple write all the Vector3 from the PCLManager to a .xyz file using:

OutputStream os = new FileOutputStream(file);
size = pointCloud.size();
for (int i = 0; i < size; i++) {
    String row = String.valueOf(pointCloud.get(i).x) + " " 
               + String.valueOf(pointCloud.get(i).y) + " " 
               + String.valueOf(pointCloud.get(i).z) + "\r\n";
    os.write(row.getBytes());
}
os.close();

Everything works fine, not compilation errors or crashes. The only thing that seems to be going wrong is the rotation or translation of the points in the cloud. When I view the point cloud everything is messed up; the area I scanned is not recognizable, though the amount of points is the same as recorded.

Could this have to do something with the fact that I don't use PoseData together with the XyzIjData? I'm kind of new to this subject and have a hard time understanding what the PoseData exactly does. Could someone explain it to me and help me fix my point cloud?

Answer 1

Yes, you have to use TangoPoseData.

I guess you are using TangoXyzIjData correctly; but the data you get this way is relative to where the device is and how the device is tilted when you take the shot.

Here's how i solved this:
I started from java_point_to_point_example. In this example they get the coords of 2 different points with 2 different coordinate system and then write those coordinates wrt the base Coordinate frame pair.

First of all you have to setup your exstrinsics, so you'll be able to perform all the transformations you'll need. To do that I call mExstrinsics = setupExtrinsics(mTango) function at the end of my setTangoListener() function. Here's the code (that you can find also in the example I linked above).

private DeviceExtrinsics setupExtrinsics(Tango mTango) {
    //camera to IMU tranform
    TangoCoordinateFramePair framePair = new TangoCoordinateFramePair();
    framePair.baseFrame = TangoPoseData.COORDINATE_FRAME_IMU;
    framePair.targetFrame = TangoPoseData.COORDINATE_FRAME_CAMERA_COLOR;
    TangoPoseData imu_T_rgb = mTango.getPoseAtTime(0.0,framePair);
    //IMU to device transform
    framePair.targetFrame = TangoPoseData.COORDINATE_FRAME_DEVICE;
    TangoPoseData imu_T_device = mTango.getPoseAtTime(0.0,framePair);
    //IMU to depth transform
    framePair.targetFrame = TangoPoseData.COORDINATE_FRAME_CAMERA_DEPTH;
    TangoPoseData imu_T_depth = mTango.getPoseAtTime(0.0,framePair);
    return new DeviceExtrinsics(imu_T_device,imu_T_rgb,imu_T_depth);
}

Then when you get the point Cloud you have to "normalize" it. Using your exstrinsics is pretty simple:

public ArrayList<Vector3> normalize(TangoXyzIjData cloud, TangoPoseData cameraPose, DeviceExtrinsics extrinsics) {
    ArrayList<Vector3> normalizedCloud = new ArrayList<>();

    TangoPoseData camera_T_imu = ScenePoseCalculator.matrixToTangoPose(extrinsics.getDeviceTDepthCamera());

    while (cloud.xyz.hasRemaining()) {
        Vector3 rotatedV = ScenePoseCalculator.getPointInEngineFrame(
                new Vector3(cloud.xyz.get(),cloud.xyz.get(),cloud.xyz.get()),
                camera_T_imu,
                cameraPose
        );
        normalizedCloud.add(rotatedV);
    }

    return normalizedCloud;
}

This should be enough, now you have a point cloud wrt you base frame of reference. If you overimpose two or more of this "normalized" cloud you can get the 3D representation of your room.

There is another way to do this with rotation matrix, explained here.

My solution is pretty slow (it takes around 700ms to the dev kit to normalize a cloud of ~3000 points), so it is not suitable for a real time application for 3D reconstruction.

Atm i'm trying to use Tango 3D Reconstruction Library in C using NDK and JNI. The library is well documented but it is very painful to set up your environment and start using JNI. (I'm stuck at the moment in fact).

Drifting

There still is a problem when I turn around with the device. It seems that the point cloud spreads out a lot.

I guess you are experiencing some drifting.
Drifting happens when you use Motion Tracking alone: it consist of a lot of very small error in estimating your Pose that all together cause a big error in your pose relative to the world. For instance if you take your tango device and you walk in a circle tracking your TangoPoseData and then you draw you trajectory in a spreadsheet or whatever you want you'll notice that the Tablet will never return at his starting point because he is drifting away.
Solution to that is using Area Learning. If you have no clear ideas about this topic i'll suggest watching this talk from Google I/O 2016. It will cover lots of point and give you a nice introduction.

Using area learning is quite simple.
You have just to change your base frame of reference in TangoPoseData.COORDINATE_FRAME_AREA_DESCRIPTION. In this way you tell your Tango to estimate his pose not wrt on where it was when you launched the app but wrt some fixed point in the area. Here's my code:

private static final ArrayList<TangoCoordinateFramePair> FRAME_PAIRS = 
    new ArrayList<TangoCoordinateFramePair>();
{
    FRAME_PAIRS.add(new TangoCoordinateFramePair(
            TangoPoseData.COORDINATE_FRAME_AREA_DESCRIPTION,
            TangoPoseData.COORDINATE_FRAME_DEVICE
    ));
}

Now you can use this FRAME_PAIRS as usual.

Then you have to modify your TangoConfig in order to issue Tango to use Area Learning using the key TangoConfig.KEY_BOOLEAN_DRIFT_CORRECTION. Remember that when using TangoConfig.KEY_BOOLEAN_DRIFT_CORRECTION you CAN'T use learningmode and load ADF (area description file).
So you cant use:

• TangoConfig.KEY_BOOLEAN_LEARNINGMODE
• TangoConfig.KEY_STRING_AREADESCRIPTION

Here's how I initialize TangoConfig in my app:

TangoConfig config = tango.getConfig(TangoConfig.CONFIG_TYPE_DEFAULT);
//Turning depth sensor on.
config.putBoolean(TangoConfig.KEY_BOOLEAN_DEPTH, true);
//Turning motiontracking on.
config.putBoolean(TangoConfig.KEY_BOOLEAN_MOTIONTRACKING,true);
//If tango gets stuck he tries to autorecover himself.
config.putBoolean(TangoConfig.KEY_BOOLEAN_AUTORECOVERY,true);
//Tango tries to store and remember places and rooms,
//this is used to reduce drifting.
config.putBoolean(TangoConfig.KEY_BOOLEAN_DRIFT_CORRECTION,true);
//Turns the color camera on.
config.putBoolean(TangoConfig.KEY_BOOLEAN_COLORCAMERA, true); 

Using this technique you'll get rid of those spreads.

PS
In the Talk i linked above, at around 22:35 they show you how to port your application to Area Learning. In their example they use TangoConfig.KEY_BOOLEAN_ENABLE_DRIFT_CORRECTION. This key does not exist anymore (at least in Java API). Use TangoConfig.KEY_BOOLEAN_DRIFT_CORRECTION instead.