Is there a way to obtain a relative rotation from core motion?
What I need is: how much it rotated in one axis and which direction (+ sign = anti-clockwise, - = clockwise, according to the right-hand rule).
I have found the property rotationRate, but I am now sure how I would extract the angle out of it, as this is giving me radians per second.
I have done all kind of stuff on the last days but nothing is giving me stable values. I have tried to do a timed sample of core motion data, using a NSTimer and calculate the difference between two samples, so I would have how much it rotated since the last sample, but from times to times it gives me crazy numbers like 13600 degrees even when the iPhone is resting on the table.
Any thoughts on how this can be accomplished?
thanks
There is indeed. You can get what you're looking for by drilling down into the properties of CMMotionManager, through CMDeviceMotion and finally to CMAttitude. The attitude of the device is defined as:
the orientation of a body relative to
a given frame of reference.
In the case of DeviceMotion's CMAttitude, that frame of reference is established by the framework when starting device motion updates. From that point in time on, the attitude of the device is reported relative to that reference frame (not relative to the previous frame).
The CMAttitude class provides some handy built in functionality to convert a CMAttitude to a form that is actually useful for something, like Euler Angles, a rotation matrix, or a quaternion. You sound like you're looking for the Euler Angle representation (Pitch, Yaw, Roll).
The answer provided above isn't quite accurate, though it's probably sufficient to answer this question. Core Motion tries to determine the device's absolute attitude at all times, meaning that the definition of the axes can vary depending on the device's orientation. For example, if the device is face-up, then pitch up/down is a rotation about the y-axis, but if the device is in landscape orientation, then pitch is a rotation about the z-axis (perpendicular to the plane of the screen). This is somewhat helpful if your application will only be used in one orientation, or you want a delta like the question asked for, but makes it excessively complicated if you want to know absolute orientation.
Related
I have a record session for my application. When user started a record session I start collecting data from device's CMMotionManager object and store them on CoreData to process and present later. The data I'm collecting includes gps data, accelerometer data and gyro data. The frequency of data is 10Hz.
Currently I'm struggling to calculate the lean angle of device with motion data. It is possible to calculate which side of device is land by using gravity data but I want to calculate right or left angle between user and ground regardless of travel direction.
This problem requires some linear algebra knowledge to solve. For example for calculation on some point I must calculate the equation of a 3D line on a calculated plane. I am working on this one for a day and it's getting more complex. I'm not good at math at all. Some math examples related to the problem is appreciated too.
It depends on what you want to do with the collected data and what ways the user will go with that recording iPhone in her/his pocket. The reason is that Euler angles are no safe and especially no unique way to express a rotation. Consider a situation where the user puts the phone upright into his jeans' back pocket and then turns left around 90°. Because CMAttitude is related to a device lying flat on the table, you have two subsequent rotations for (pitch=x, roll=y, yaw=z) according to this picture:
pitch +90° for getting the phone upright => (90, 0, 0)
roll +90° for turning left => (90, 90, 0)
But you can get the same position by:
yaw +90° for turning the phone left (0, 0, 90)
pitch -90° for making the phone upright (-90, 0, 90)
You see two different representations (90, 90, 0) and (-90, 0, 90) for getting to the same rotation and there are more of them. So you press Start button, do some fancy rotations to put the phone into the pocket and you are in trouble because you can't rely on Euler Angles when doing more complex motions (s. gimbal lock for more headaches on this ;-)
Now the good news: you are right linear algebra will do the job. What you can do is force your users to put the phone in always the same position e.g. fixed upright in the right back pocket and calculate the angle(s) relative to the ground by building the dot product of gravity vector from CMDeviceMotion g = (x, y, z) and the postion vector p which is the -Y axis (0, -1, 0) in upright position:
g • x = x*0 + y*(-1) + z*0 = -y = ||g||*1*cos (alpha)
=> alpha = arccos (-y/9.81) as total angle. Note that gravitational acceleration g is constantly about 9.81
To get the left-right lean angle and forward-back angle we use the tangens:
alphaLR = arctan (x/y)
alphaFB = arctan (z/y)
[UPDATE:]
If you can't rely on having the phone at a predefined postion like (0, -1, 0) in the equations above, you can only calculate the total angle but not the specific ones alphaLR and alphaFB. The reason is that you only have one axis of the new coordinate system where you need two of them. The new Y axis y' will then be defined as average gravity vector but you don't know your new X axis because every vector perpedicular to y' will be valid.
So you have to provide further information like let the users walk a longer distance into one direction without deviating and use GPS and magnetometer data to get the 2nd axis z'. Sounds pretty error prone in practise.
The total angle is no problem as we can replace (0, -1, 0) with the average gravity vector (pX, pY, pZ):
g•p = xpX + ypY + zpZ = ||g||||p||*cos(alpha) = ||g||^2*cos(alpha)
alpha = arccos ((xpX + ypY + z*pZ) / 9.81^2)
Two more things to bear in mind:
Different persons wear different trowsers with different pockets. So the gravity vector will be different even for the same person wearing other clothes and you might need some kind of normalisation
CMMotionManager does not work in the background i.e. the users must not push the standby button
If I understand your question, I think you are interested in getting the attitude of your device. You can do this using the attitude property of the CMDeviceMotion object that you get from the deviceMotion property of the CMMotionManager object.
There are two different angles that you might be interested in the CMAttitude class: roll and pitch. If you imagine your device as an airplane with the propeller at the top (where the headphone jack is), pitch is the angle the plane/device would make with the ground if the plane were in a climb or dive. Meanwhile, roll is the angle that the "wings" would make with the ground if the plane were to be banking or in mid barrel roll.
(BTW, there is a third angle called yaw that I think is not relevant for your question.)
The angles will be given in radians, but it's easy enough to convert them to degrees if that's what you want (by multiplying by 180 and then dividing by pi).
Assuming I understand what you want, the good news is that you may not need to understand any linear algebra to capture and use these angles. (If I'm missing something, please clarify and I'd be happy to help further.)
UPDATE (based on comments):
The attitude values in the CMAttitude object are relative to the ground (i.e., the default reference frame has the Z-axis as vertical, that is pointing in the opposite direction as gravity), so you don't have to worry about cancelling out gravity. So, for example, if you lie your device on a flat table top, and then roll it up onto its side, the roll property of the CMAttitude object will change from 0 to plus or minus 90 degrees (+- .5pi radians), depending on which side you roll it onto. Meanwhile, if you start it lying flat and then gradually stand it up on its end, the same will happen to the pitch property.
While you can use the pitch, roll, and yaw angles directly if you want, you can also set a different reference frame (e.g., a different direction for "up"). To do this, just capture the attitude in that orientation during a "calibration" step and then use CMAttitude's multiplyByInverseOfAttitude: method to transform your attitude data to the new reference frame.
Even though your question only mentioned capturing the "lean angle" (with the ground), you will probably want to capture at least 2 of the 3 attitude angles (e.g., pitch and either roll or yaw, depending on what they are doing), potentially all three, if the device is going to be in a person's pocket. (The device could rotate in the pocket in various ways if the pocket is baggy, for example.) For the most part, though, I think you will probably be able to rely on just two of the three (unless you see radical shifts in yaw throughout the course of a recording session). So for example, in my jeans pocket, the phone is usually nearly vertical. Thus, for me, pitch would vary a whole bunch as I, say, walk, sit or run. Roll would vary whenever I change the direction I'm facing. Meanwhile, yaw would not vary much at all (unless I do kart-wheels, which I can't!). So yaw can probably be ignored for me.
To summarize the main point: to use these attitude angles, you don't need to do any linear algebra, nor worry about gravity (although you may want to use this for other purposes, of course).
UPDATE 2 (based on Kay's new post):
Kay just replied and showed how to use gravity and linear algebra to make sure your angles are unique. (And, btw, I think you should give the bounty to that post, fwiw.)
Depending on what you want to do, you may want to use this math. You would want to use the linear algebra and gravity if you need a standardized way of "talking about" and/or comparing attitudes over the course of your recording session. If you just want to visualize them, you can probably still get away with not using the increased complexity. (For example, visualizing (pitch=90, roll=0, yaw=0) should be the same as visualizing (pitch=0, roll=90, yaw=90).) In my approach above, while you could have multiple ways of referring to the "same" attitude, none of them is actually wrong, per se. They will still give you the angles relative to the ground.
But the fact that the gyroscope can switch from one valid description of an attitude to another means that what I wrote above about getting away with only 2 of the 3 components needs to be corrected: because of this, you will need to capture all three components, no matter what. Sorry.
I am relatively new to iPhone development and I am playing with the gyroscope, using Core Motion. After a few tests, this is my question.
What information is exactly the gyroscope measuring? absolute angles? I mean, suppose I hold my phone in portrait, at exactly 90 degrees and start sampling. These may be not the correct values, but suppose that at this position, the gyroscope gives me 0, 0 and 0 degrees for yaw, pitch and roll.
Now I throw my iphone in the air and as it goes up it rolls at random a high number of full turns in all axis and returns to my hand at the same position as before. Will the gyroscope read 0,0,0 (meaning that it has the same position as before = absolute angle) or not?
If not, there's a way to measure absolute degrees in all axis? As absolute degrees I mean assuming 0,0,0 as the position it was when the sampling started.
thanks
The gyroscope measures many things for you, and yes, one of these is "absolute angles". Take a look at the docs on CMDeviceMotion. It can give you a rotation rate, which is how fast the device is spinning, and it can give you a CMAttitude. The CMAttitude is what you're calling "absolute angles". It is technically defined as:
the orientation of a body relative to
a given frame of reference
The really nice thing is that normal gyroscopes, as noted in the other answer, are prone to drift. The Core Motion framework does a lot of processing behind the scened for you in an effort to compensate for the drift before the measurements are reported. Practically, I've found that the framework does a remarkable (though not perfect) job at this task. Unless you need long term precision to a magnetic pole or something, the attitude reported by the framework can be considered as a perfect relative attitude measurement, for all intents and purposes.
The iPhone uses accelerometers for its internal angle measurements, which means they are relative to the Earth's gravity. That's about as absolute as you're going to get, unless you need this program to work in space, too.
Games like FroggyJump for iPhone figure out the rotation of the iphone. I'm getting confused with the acceleration values. How do I calculate the level of rotation? I suppose I need to consider when the iphone isn't perfectly upright.
Thank you.
I'm also wanting to use the new Core Motion framework with the "Device Motion" for iPhone 4 for extra precision. I guess I'll have to use that low pass filter for the other devices.
It's the yaw.
Having given Froggy Jump a quick go, I think it's likely directly using the accelerometer's x value as the left/right acceleration on the frog. If it is stationary, you can think of an accelerometer as giving you the vector that points upward into space, relative to the local axes. For something like a ball rolling or anything else accelerating due to tilt, you want to use the values directly.
For anything that involves actually knowing angles, you're probably best picking the axis around which you want to detect rotation then using the C function atan2f on the accelerometer values for the other two axes. With just an accelerometer, there are some scenarios in which you can't detect rotation — for example, if the device is flat on a table then an accelerometer can't detect yaw. The general rule is that rotations around the gravity vector can't be detected with an accelerometer alone.
I'm concepting an iPhone app that will require precise calibration to the iPhones accelerometer and gyro data. I will have to simulate specific movements that I would eventually like to execute code. (Think shake-to-shuffle, or undo).
Is there a good way of doing this already? or something you can come up with? Perhaps some way to generate a time/value graph of the movement data as it is being captured?
Movement data being captured - see the accelerometer graph sample app, which shows the data in real time: http://developer.apple.com/library/ios/#samplecode/AccelerometerGraph/Introduction/Intro.html
The data is pretty noisy - the gyro and accelerometer aren't good enough right now to be able to track where the phone is in local 3d space, for example. The rotation, however, is very solid, and the orientation of the device can be pretty accurately tracked. You may have the best results making gestures out of rotation data instead of movement along an axis. Or, basic direction like shakes along an axis will work as Jacob Jennings said.
A good starting point for accelerometer gesture recognition is this tutorial by Kevin Bomberry at AblePear:
http://blog.ablepear.com/2010/02/iphone-sdk-shake-rattle-roll.html
He sets a blanket threshold for the absolute value of acceleration on any axis. I would generate an 'event' for the axis that had the highest acceleration during the break of the threshold (Z POSITIVE, X NEGATIVE, etc), and push these on an 'event history' queue. At the end of each didAccelerate call, evaluate the queue for patterns that match a gesture, for example:
X POSITIVE, X NEGATIVE, X POSITIVE, X NEGATIVE might be considered a 'shake' along that axis. This should provide a couple different gesture commands.
See the following for a simple queue category addition to NSMutableArray:
How do I make and use a Queue in Objective-C?
I have a xyz accelerometer and magnetometer. Now I want to determine the orientation of the device using both. The problem I see is that depending on the device orientation, I'd need to use the sensors in different order.
Let me give an example. If I have the device facing me then changes in both the roll and pitch can be determined with the accelerometer. For yaw I use the magnetometer.
But if I put the device horizontally (ie. turn it 90º, facing the ceiling) then any change in the up vector (now horizontal) isn't notice, as the accelerometer doesn't detect any change. This can now be detected with the magnetometer.
So the question is, how to determine when to use one or the other. Is this enough with both sensors or do I need something else?
Thanks
The key is to use the cross product of the two vectors, gravity and magnetometer. The cross product gives a new vector perpendicular to them both. That means it is horizontal (perpendicular to down) and 90 degrees away from north. Now you have three orthogonal vectors which define orientation. It is a little ugly because they are not all perpendicular but that is easy to fix. If you then cross this new vector back with the gravity vector that gives a third vector perpendicular to the gravity vector and the magnet plane vector. Now you have three perpendicular vectors which defines your 3D orientation coordinate system. The original accelerometer (gravity) vector defines Z (up/down) and the two cross product vectors define the east/west and north/south components of the orientation.
Here is some documentation that walks through this project. As is clear from other answers, the math can be tricky.
http://www.freescale.com/files/sensors/doc/app_note/AN4248.pdf
I think the question "how to determine when to use one or the other" is misguided. You should always use both sensors for orientation. There are cases where one of them is useless. However, these are edge cases.
If I understand you correctly, you'll need something to detect pitch (tilting) and orientation according to the cardinal points (North, East, South and West).
The pitch can be read from the accelerometer.
The orientation according to the cardinal points can be read from a compass.
Combining the output from these two sensors correctly with the right math in your software will most likely give you the absolute orientation.
I think it's doable that way.
Good luck.
In the event you still need absolute orientation you can check this break out board from Adafruit: https://www.adafruit.com/products/2472. The nice thing about this is board is that it has an ARM Cortex-M0 processor to do all of the calculations for you.