I'd like to track the position of the device with respect to an initial position with high accuracy (ideally) for motions at a small scale (say < 1 meter). The best bet seems to be using motionReading.SensorReading.DeviceAcceleration. I tried this. But ran into few problems. Apart from the noisy readings (which I was expecting and can tolerate), I see some behaviors that are conceptually wrong - e.g. If I start from rest, move the phone around and bring it back to rest- and in the process periodically update the velocity vector along all the dimensions, I would expect the magnitude of the velocity to be very small (ideally 0). But I don't see that. I have extensively reviewed available help including the official msdn pages but I don't see any examples where the position/velocity of the device are updated using the acceleration vector. Is the acceleration vector that the api returns (atleast in theory) supposed to be the rate of change of velocity or something else? (FYI - my device does not have a gyroscope, so the api is going to be the low accuracy version.)
Related
I'm looking for more information on how to use drift-correction correctly (using Unity SDK).
On the Tango website it says "Drift-corrected frames come through the Area Description reference frame", that the frame pair Start Of Service -> Device "does not include drift correction" and for Area Description -> Start Of Service that it "provides updates only when a localization event or a drift correction occurs".
The way I'd like to use a drift-corrected pose is like in the TangoPointCloud prefab, where depth points are multiplied by a matrix startServiceTDevice which results from the frame pair SoS -> Device. Assuming that the drift-corrected frame is in the AD frame, I'd need SoS -> AD. Since only AD -> SoS is available, I tried with this one and its inverse. The resulting pose is too small though to make any sense (even if using it the wrong direction, the translation shouldn't be close to zero if I had been walking around). Then I considered that the AD frame might actually be something like a drift-corrected Start of Service, but then again I can't find any significant/visible difference between AD -> Device and SoS -> Device, definitely no loop closures in it. I'm requesting and applying poses after finishing my scan, so drifts should have been detected by then.
On the Tango website it's further said that "There will be a period after Startup during which drift-corrected frames are not available.", yet the AD -> SoS pose is available (and valid) from the beginning and I couldn't yet produce a situation where it wasn't (e.g. no motion, rapid motion...).
Is drift correction working at all? Or am I using it all wrong?
PS: On the latest stackoverflow post it sounds as if drift correction would be for relocalization after tracking loss only. However, I find this hard to believe since the Tango website describes drift correction as "When the device sees a place it knows it has seen earlier in your session, it realizes it has traveled in a loop and adjusts its path to be more consistent with its previous observations.".
Drift correction is working as experimental features at this moment, there's corner cases that it will break. I will go into more details later.
In order to use drift correction pose, you will need to use ADF_T_Device frame pair (ADF is base frame, Device is target frame). In the example of using drift-correction pose to project points into world space, you don't need to do Adf_T_ss * ss_T_device transform, instead, all you only need to use ADF_T_device frame directly. If this is in Unity, you can just check the use area description pose on PointCloud prefab.
Corner cases that breaks drift-correction:
User shakes the device right after starting the experience.
Under the hood, drift correction is constructing a more dense but more accurate version of ADF. If user covers camera or shake device at the very beginning, that will cause that no ADF (or features) being saved in the buffer. Thus the API could get into a state that never gives any valid pose from ADF_T_Device frame pair.
Device lost tracking, and user moved to a new space without relocalizing.
This is similar to the first case. If user moved to a new space without relocalizing after lost tracking, device will never relocalized, thus no valid pose will be available through ADF_T_device frame.
Drift correction API is still experimental, we are trying to address above issues from API level as well.
I am getting my first Tango in the next day or so; worked a little bit with Occipital's Structure Sensor - which is where my background in depth perceiving camera's come from.
Has anyone used multiple Tango at once (lets say 6-10), looking at the same part of a room, using depth for identification and placement of 3d character/content? I have been told that multiple devices looking at the same part of a room will confuse each Tango as they will see the other Tango's IR dots.
Thanks for your input.
Grisly
I have not tried to use several Tangos, but I have however tried to use my Tango in a room where I had a Kinect 2 sensor, which caused the Tango to go bananas. It seems however like the Tango has lower intensity on its IR projector in comparison, but I would still say that it is a reasonable assumption that it will not work.
It might work under certain angles but I doubt that you will be able to find a configuration of that many cameras without any of them interfering with each other. If you would make it work however, I would be very interested to know how.
You could lower the depth camera rate (defaults to 5/second I believe) to avoid conflicts, but that might not be desirable given what you're using the system for.
Alternatively, only enable the depth camera when placing your 3D models on surfaces, then disable said depth camera when it is not needed. This can also help conserve CPU and battery power.
It did not work. Occipital Structure Sensor on the other hand, did work (multiple devices in one place)!
Is there anyway to disable the "inertial motion sensors" for a program, so that my program does not track the devices acceleration? I've noticed that if a user suddenly moves and then suddenly stops with the device the motion tracking becomes inaccurate.
Even if you could, "disabling the sensors" is not a good idea. And when you say "disabling" I assume you mean setting them to an initialization state rather than just ignoring the data stream. The 3-axis accelerometer and gyroscope data are fused with position data to provide your relative location during motion tracking. You have no way of knowing which of these data streams is the source of the inaccuracy, and just turning off acceleration would likely require a re-calibration of all sensors so that tracking (data fusion) is accurate.
Replicate the error with as much data as possible (speed, stopping time, orientation of the tablet, distance to the nearest object, nearest object characteristics, etc.) and report it to the project Tango team.
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
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.
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
I am hacking a vacuum cleaner robot to control it with a microcontroller (Arduino). I want to make it more efficient when cleaning a room. For now, it just go straight and turn when it hits something.
But I have trouble finding the best algorithm or method to use to know its position in the room. I am looking for an idea that stays cheap (less than $100) and not to complex (one that don't require a PhD thesis in computer vision). I can add some discrete markers in the room if necessary.
Right now, my robot has:
One webcam
Three proximity sensors (around 1 meter range)
Compass (no used for now)
Wi-Fi
Its speed can vary if the battery is full or nearly empty
A netbook Eee PC is embedded on the robot
Do you have any idea for doing this? Does any standard method exist for these kind of problems?
Note: if this question belongs on another website, please move it, I couldn't find a better place than Stack Overflow.
The problem of figuring out a robot's position in its environment is called localization. Computer science researchers have been trying to solve this problem for many years, with limited success. One problem is that you need reasonably good sensory input to figure out where you are, and sensory input from webcams (i.e. computer vision) is far from a solved problem.
If that didn't scare you off: one of the approaches to localization that I find easiest to understand is particle filtering. The idea goes something like this:
You keep track of a bunch of particles, each of which represents one possible location in the environment.
Each particle also has an associated probability that tells you how confident you are that the particle really represents your true location in the environment.
When you start off, all of these particles might be distributed uniformly throughout your environment and be given equal probabilities. Here the robot is gray and the particles are green.
When your robot moves, you move each particle. You might also degrade each particle's probability to represent the uncertainty in how the motors actually move the robot.
When your robot observes something (e.g. a landmark seen with the webcam, a wifi signal, etc.) you can increase the probability of particles that agree with that observation.
You might also want to periodically replace the lowest-probability particles with new particles based on observations.
To decide where the robot actually is, you can either use the particle with the highest probability, the highest-probability cluster, the weighted average of all particles, etc.
If you search around a bit, you'll find plenty of examples: e.g. a video of a robot using particle filtering to determine its location in a small room.
Particle filtering is nice because it's pretty easy to understand. That makes implementing and tweaking it a little less difficult. There are other similar techniques (like Kalman filters) that are arguably more theoretically sound but can be harder to get your head around.
A QR Code poster in each room would not only make an interesting Modern art piece, but would be relatively easy to spot with the camera!
If you can place some markers in the room, using the camera could be an option. If 2 known markers have an angular displacement (left to right) then the camera and the markers lie on a circle whose radius is related to the measured angle between the markers. I don't recall the formula right off, but the arc segment (on that circle) between the markers will be twice the angle you see. If you have the markers at known height and the camera is at a fixed angle of inclination, you can compute the distance to the markers. Either of these methods alone can nail down your position given enough markers. Using both will help do it with fewer markers.
Unfortunately, those methods are imperfect due to measurement errors. You get around this by using a Kalman estimator to incorporate multiple noisy measurements to arrive at a good position estimate - you can then feed in some dead reckoning information (which is also imperfect) to refine it further. This part is goes pretty deep into math, but I'd say it's a requirement to do a great job at what you're attempting. You can do OK without it, but if you want an optimal solution (in terms of best position estimate for given input) there is no better way. If you actually want a career in autonomous robotics, this will play large in your future. (
Once you can determine your position you can cover the room in any pattern you'd like. Keep using the bump sensor to help construct a map of obstacles and then you'll need to devise a way to scan incorporating the obstacles.
Not sure if you've got the math background yet, but here is the book:
http://books.google.com/books/about/Applied_optimal_estimation.html?id=KlFrn8lpPP0C
This doesn't replace the accepted answer (which is great, thanks!) but I might recommend getting a Kinect and use that instead of your webcam, either through Microsoft's recently released official drivers or using the hacked drivers if your EeePC doesn't have Windows 7 (presumably it does not).
That way the positioning will be improved by the 3D vision. Observing landmarks will now tell you how far away the landmark is, and not just where in the visual field that landmark is located.
Regardless, the accepted answer doesn't really address how to pick out landmarks in the visual field, and simply assumes that you can. While the Kinect drivers may already have feature detection included (I'm not sure) you can also use OpenCV for detecting features in the image.
One solution would be to use a strategy similar to "flood fill" (wikipedia). To get the controller to accurately perform sweeps, it needs a sense of distance. You can calibrate your bot using the proximity sensors: e.g. run motor for 1 sec = xx change in proximity. With that info, you can move your bot for an exact distance, and continue sweeping the room using flood fill.
Assuming you are not looking for a generalised solution, you may actually know the room's shape, size, potential obstacle locations, etc. When the bot exists the factory there is no info about its future operating environment, which kind of forces it to be inefficient from the outset.
If that's you case, you can hardcode that info, and then use basic measurements (ie. rotary encoders on wheels + compass) to precisely figure out its location in the room/house. No need for wifi triangulation or crazy sensor setups in my opinion. At least for a start.
Ever considered GPS? Every position on earth has a unique GPS coordinates - with resolution of 1 to 3 metres, and doing differential GPS you can go down to sub-10 cm range - more info here:
http://en.wikipedia.org/wiki/Global_Positioning_System
And Arduino does have lots of options of GPS-modules:
http://www.arduino.cc/playground/Tutorials/GPS
After you have collected all the key coordinates points of the house, you can then write the routine for the arduino to move the robot from point to point (as collected above) - assuming it will do all those obstacles avoidance stuff.
More information can be found here:
http://www.google.com/search?q=GPS+localization+robots&num=100
And inside the list I found this - specifically for your case: Arduino + GPS + localization:
http://www.youtube.com/watch?v=u7evnfTAVyM
I was thinking about this problem too. But I don't understand why you can't just triangulate? Have two or three beacons (e.g. IR LEDs of different frequencies) and a IR rotating sensor 'eye' on a servo. You could then get an almost constant fix on your position. I expect the accuracy would be in low cm range and it would be cheap. You can then map anything you bump into easily.
Maybe you could also use any interruption in the beacon beams to plot objects that are quite far from the robot too.
You have a camera you said ? Did you consider looking at the ceiling ? There is little chance that two rooms have identical dimensions, so you can identify in which room you are, position in the room can be computed from angular distance to the borders of the ceiling and direction can probably be extracted by the position of doors.
This will require some image processing but the vacuum cleaner moving slowly to be efficiently cleaning will have enough time to compute.
Good luck !
Use Ultra Sonic Sensor HC-SR04 or similar.
As above told sense the walls distance from robot with sensors and room part with QR code.
When your are near to a wall turn 90 degree and move as width of your robot and again turn 90deg( i.e. 90 deg left turn) and again move your robot I think it will help :)