I want to find the cardinal direction accelerated by an iphone. I thought I could just use the accelerometer to do this, however, as you can see from the picture below the accelerometers axes are defined by the device orientation.
I figured that if i used the gyroscope to correct for yaw, spin, rotation then I could get a more accurate reading and not have to hold the phone in the same orientation during movement.
But this still does not tell me what cardinal direction the iphone is moving in. For that I would also have to use the the magnetometer.
Can anybody tell me how to use a three sensor readings to find the cardinal direction accelerated in? I dont even know where to start. I dont even know if the phone takes these measurements at the same rates of time either.
Taking the cross product of the magnetometer vector with the "down" vector will give you a horizontal magnetic east/west vector; from that, a second cross product gets the magnetic north/south vector. That's the easy part.
The harder problem is tracking the "down" vector effectively. If you integrate the accelerometers over time, you can filter out the motion of a hand-held mobile device, to get the persistent direction of gravity. Or, you could, if your device weren't rotating at the same time...
That's where the rate gyros come in: the gyros can let you compensate for the dynamic rotation of the hand-held device, so you can track your gravity in real-time. The classic way to do this is called a Kalman filter, which can integrate (both literally and figuratively) multiple data sources in order to evaluate the most likely state of your system.
A Kalman filter requires a mathematical model both of your physical system, and of the sensors that observe it; each of these models must be both accurate and "sufficiently linear" for the Kalman filter to work properly. As it happens, the iphone/accelerometer/gyro system is in fact sufficiently linear.
The Kalman filter uses both calculus and linear algebra, so if you're rolling your own, you will need a certain amount of math.
Also, as a practical matter, you should understand that physical sensors typically have offsets that need to be compensated for -- in particular, you need to pay attention to the rate gyro offsets in this kind of inertial navigation system, or your tracker will never stabilize. This means you will need to add your rate gyro offsets to your Kalman state vector and system model.
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I'm new to Core Motion and I'm very confused. Can somebody please explain what these inputs measure and how they can be useful in simple terms?
Accelerometers measure movement relative to gravity, by virtue of "feeling" the force of movement applied to the device. Force of movement can be described as the rate of acceleration and deceleration of the device, hence the name of this sensor.
Gyroscopes measure changes in rotation by virtue of a suspended element reporting its rotation relative to the device. As the device rotates, this suspended element doesn't rotate, so there's a report coming from it that tells you how far the phone's rotated.
Magnetometers get their idea of rotational position from the north/south magnetic fields that compasses use to know where they are relative to the poles. This data is used (primarily) to help the Gyroscope, because these things suffer from float and inertia.
Combined, the information from these sensors, when filtered well (which Apple does for you with CoreMotion) give you all the movement of a phone.
So you can know if the user is swinging the phone around like a table tennis bat, or steering like a Wii Remote Mario style game controller, or simply walking.
I want to track user's position and update it in the offline map based on his movement without using GPS and having to rely on location updates.
I have tried CMMotionManager and got acceleration in G's. However, this is acceleration rather than valocity. The manager also allows to get gravity, rotation and attitude.
Is there a way to calculate the user's speed in m/s ? If so, how would I go about it? Any formulas / code samples?
The only way to do this is to assume that the phone is at rest when the app starts. With an accelerometer there's no way to tell the difference between being at rest and moving at a constant rate. For example if you were on a jet plane you'd have no way to tell that you were traveling at 800 kph and not sitting still.
If you do assume that you are at rest when you start it's possible to come up with very crude estimates of speed by tracking acceleration, but in practice, the results are prone to large amounts of "drift error", were small measurement errors quickly add up to a completely wrong current speed result, and so your position drifts around hopelessly.
So in practice, the answer to your question is "no, not really."
Edit:
Thinking about this a little more, you might be able to get usable results if you can impose some assumptions.
Say we assume that the user is on foot. We rule out traveling on a bike/in a car/train/plane. On foot, you really don't "drift". You move in fits and starts as the user takes steps. In fact, you could likely use the accelerometer to recognize the characteristic bounce of a person walking. There are pedometer apps that already do that. For walking, you could probably assume that in the absence of acceleration (ignoring gravity, which is constant), the phone is stationary, so zero out the speed and keep it at zero until there is an acceleration above a certain threshold. That would enable you to reduce drift error.
We have XSENS MTi IMU-Device and use the ROS-Framework (Ubuntu / Fuerte).
We subscribe to the IMU-Data and all data looks good except orientation.
In Euler-Outputmode like in Quaternion-Outputmode the values are constantly changing. Not randomly, they increase or decrease slowly at a more or less constant rate, and sometimes I observed the change to flatten out and then change it's direction.
When the Value at Second X may be:
x: 7.79210457616
y: -6.58661204898
z: 41.2841955308
the Z value changes in a range of about 10 within a few seconds (10-20 seconds I think).
What can cause this behaviour? Do we misinterpret the data or is there something wrong with the driver? The strange thing is, this also happend with 2 other drivers, and one other IMU device (we have 2). Same results in each combination.
Feel free to ask for more precise data or whatever you'd like to know that may be able to help us out. We are participating at the Spacebot-Cup in November, so it would be quite a relief to get the IMU done. :)
Perfectly normal if you have no magnetometer to give a corrected heading.
Gyroscope alone measures rate of turn only, and has no idea of orientation at any given time on any axis. Integrating the rate of turn gives the heading if you know the initial heading and the gyro is 100% accurate. It drifts anyway, even if it's perfectly calibrated, as you are sampling at discrete intervals rather than continuously.
Adding an accelerometer will at least fix the downward direction (because it measures gravity, which is towards the Earth's centre). This will keep the Z axis solution aligned with vertical, but it won't fix the horizontal direction (the heading or yaw). That will continue to drift, as you are seeing.
Adding a magnetometer will fix the heading relative to the Earth's magnetic field. This will give you a heading relative to magnetic North. You will need to apply a shift for local magnetic declination to get True North. These are generally available on line and reasonably constant over tens of km. Google ITREF.
Some integrated sensors don't have a magnetometer. That's why the heading drifts. Units like the MPU6050 have firmware built in, and can access a magnetometer, but the usual firmware doesn't use it, so you have to implement Madgwick, etc., on your micro controller or a connected PC anyway. Bosch have a new single module with a processing unit built in. Hopefully, it uses 9 DOF rather than the 6 you get with the DMP on the MPU6050.
Magnetic sensors are accurate to about 2 degrees. Local magnetic declination corrections also have an error. You may be able to perform additional calibrations by using a GPS on a long base line to get better results. It's also worth noting that heading and course made good are often different, due to crosswind / cross currents.
The Madgwick algorithm is fairly stable and easy to implement, and uses fewer resources than a Kalman filter, which needs to perform matrix inversion. It still gives minor jitter, but minor smoothing of results shouldn't induce too much lag.
If you have the IMU version, I assume that no signal processing has been done on the device. (but I don't know the product). So the data you get for the orientation should be only the integral of the gyroscope data.
The drift you can see is normal and can come from the integration of the noise, a bad zero rate calibration, or the bias of the gyroscope.
To correct this drift, we usually use an AHRS or a VRU algorithm (depending the need of a corrected yaw). It's a fusion sensor algorithm which take the gravity from the accelerometer and the magnetometer data (for AHRS) to correct this drift.
The algorithms often used are the Kalman filter and the complementary filter (Madgwick/Mahony).
It's not an easy job and request a bit of reading and experimenting on matlab/python to configure these filters :)
I captured raw magnetometer data on the iPhone 5 by accident, and I actually require the calibrated data. The problem is that I can't go and recapture the data I originally got. Does anyone know what the iPhone's hard bias (device bias) calibration values are and how I can apply them to my data to get a similar output to what the iPhone would have given me?
Alternatively what is the best approach to calibrate for the device bias? I don't care about soft bias in my measurements.
Thanks
The iPhone needs calibration data not to calibrate for the internal sensor (that is always accounted for, even in "raw" data, which actually isn't as raw as you might think). It is actually to calibrate for external factors that might disrupt or interfere with the Earth's natural magnetic field, like high voltage power lines, or steel beams overhead. The iPhone creates a 3D distortion map of the field (which is why the compass app asks you to make a figure eight) to offset these external influences.
Finally, even if you could recreate the exact distortions, Apple provides no way to peek into their black-box filtering, let alone apply your own distortion map to their data. So no, you cannot recompute the calibrated data after the fact.
I'm currently developing an iPhone App (on iPhone 5, iOS 7, Xcode 5) which requires a very accurate determination of the current attitude. The "attitude" of CMDeviceMotion does not fulfil these requirements because Apple's sensor fusion algorithm seems to rely too much on the gyroscope which drifts away rather fast (in my experience). That's why I decided to read out the bare sensor data and later I want to combine it within a sensor fusion algorithm by myself.
When asking for magnetometer data one has two possibilities:
via CMMagnetometerData in CMMotionManager
via CMCalibratedMagneticField in CMDeviceMotion about which Apple says
The CMCalibratedMagneticField returned by this property gives you the total magnetic field in the device’s vicinity without device bias. Unlike the magneticField property of the CMMagnetometer class, these values reflect the earth’s magnetic field plus surrounding fields, minus device bias.
In principle (2.) is exactly what I want.
There is a very simple test if magnetometer data is calibrated properly. For simplicity one can restrict oneself to two dimensions. When the device lies on it's back, the combination B_x^2 + B_y^2 must be constant, independent of the direction the device is pointing to. It must just equal the horizontal component of the Earth's magnetic field (assuming no other fields in the vicinity of the device). Thus, when performing a 360 degrees turn of the device which lies on it's back, the measured data B_y over B_x should display a circle. See here for details.
Now the point: the data of CMCalibratedMagneticField does NOT result in a circle!
Does anyone have an explanation for that? Or does anyone know, how the CMCalibratedMagneticField comes about? Is the magnetometer calibrated in the sense of the link from above when performing the "eight-shaped" movement of the device or what is the movement good for?
Btw. why the "eight-shaped" movement and not flipping the device around it's three axis, which would allow a calibration as described in the link from above?
I would be very glad for any clarification with this issue... Thanks!
There is a problem with the magnetometer in iOS 7, it has an error of +-7º. Try using the 7.1 beta version.
EDIT
The magnetometer has zero-drift over time, but is pretty inaccurate for sudden changes in position. The accelerometer and gyroscope on the other hand adjust quickly for sudden changes but, being inertial sensors, they lose accuracy over a period of time.
So when CMCalibratedMagneticField tries compensate for your rotational motion it uses data from the gyroscope and accelerometer. This is when the accelerometer and gyroscope's +-7º error creeps in and throws your circle off track. Check this answer and this wikipedia article for more info.
As regards to the figure of eight:
Both do the same thing, they orient the "North" of your device in each direction in hope of cancelling out magnetic interference. Flipping your device along all three axes will work better but it is harder to perform and not as easily understood by the user.
Hope this helps.