问题
I’m starting to develop with Project Tango API.
I need to save PointCloud data that I get in the event OnXyzIjAvailable; to do this, I started from your example "PointCloudJava" and wrote PointCloud coordinates in single files (an AsyncTask is started for this purpose). So I have one file with xyz for each event. On the same event I get the corresponding transformation matrix (mRenderer.getModelMatCalculator(). GetPointCloudModelMatrixCopy()).
Point clouds
Then I’ve imported all this data (xyz point cloud with corresponding transformation matrix; the transformation matrix is applied to the point clouds) but the point clouds doesn’t match exactly; it seems that point clouds are closed each other but not overlapping exactly.
My questions are: -Why I don’t have the matching between the single point clouds ? -What I should have to do to have this matching ?
Then I’ve notice the following that is probably related to the above problem; I’ve used Project Tango Explore application (Area learning), I can see my position, but is constantly in motion even if I don't move. Which is the problem ? Is it necessary a calibration?
Device Information
回答1:
Poses delivered by Tango have a non-negligible amount of drift. Here is a sample graph of pose position when my tablet was in its stand observing a static scene (ideally the traces should be flat):
When we couple this drift with tracking errors when the device is actually moving then this produces noticeable registration issues. I see this especially when the device is rolled, i.e. rotated about the view axis. The raw pose quality may be sufficient for some applications (e.g. location) but causes problems for others (e.g. 3D scanning, seamless augmented reality).
I was disappointed when I saw this. But if Tango is attempting to measure motion by using the fisheye camera to correct inertial motion prediction - and not by using stereo vision between the fisheye and color cameras - then that is a really hard problem. And the reason for doing that would be to stay within CPU/GPU/RAM/latency/battery budgets to leave something for applications. So after consideration, while I remain disappointed, I can understand it.
I am hopeful that Tango will improve their pose algorithm over time, but I suspect that applications that depend on precise tracking will still have to add their own corrections, e.g. via stereo, structure from motion, point cloud correlation, etc.
回答2:
Point clouds should be viewed as statistically accurate, not exactly accurate - there is a distance estimation error range that is a function of distance and surface characteristics - a tango fixed in a specific location will not return a constant point clout - rotation of the device can cause apparent drift, but it really isn't, it's just that the error is rotating along with the tango
来源：https://stackoverflow.com/questions/30194001/transforming-and-registering-point-clouds