As this question has been asked already, i didnt get any clear idea in it. So i am asking this question again.I am struggling with it for two days.
I want to calculate the distance between camera of my devices with that of the object that are infront of camera.
I tried to use sensor managers as it was suggested in the following link but i am getting wrong results in it.
I have used two sensors accelerometer and magnetic field.
I calculate the distance based on orientation of the device as suggested in the above link.
onSensorChanged Callback in Sensor listener
#Override
public void onSensorChanged(SensorEvent event) {
if (event.sensor.getType() == Sensor.TYPE_ACCELEROMETER)
gravity = event.values.clone();
if (event.sensor.getType() == Sensor.TYPE_MAGNETIC_FIELD)
geoMagnetic = event.values.clone();
if (gravity != null && geoMagnetic != null) {
float R[] = new float[9];
float I[] = new float[9];
boolean success = SensorManager.getRotationMatrix(R, I, gravity,
geoMagnetic);
if (success) {
float orientation[] = new float[3];
SensorManager.getOrientation(R, orientation);
azimuth = 57.29578F * orientation[0];
pitch = 57.29578F * orientation[1];
roll = 57.29578F * orientation[2];
}
}
}
Portrait:
double dist = Math.abs((float) (1.4f * Math.tan(roll * Math.PI / 180)));
Landscape:
double dist = Math.abs((float) (1.4f * Math.tan(pitch* Math.PI / 180)));
Please provide information about how to calculate the distance. Any ideas and suggestion are appreciable,
Thanks in advance.
I'm having a really annoying problem with a AR view acting like a compass. So when I hold the phone in portrait (so that the screen is pointing to my face), then I call the remapCoordinateSystem that the pitch is 0 when holding it portrait. Then the azimuth (compass functionality) is perfect, but as soon as I tilt the phone the azimuth gets ruined, if I bend forward the azimuth increases and if I bend backwards it decreases.
I use 2 sensors to get the readings, Sensor.TYPE_MAGNETIC_FIELD and Sensor.TYPE_GRAVITY.
I use a lowpassfilter which is pretty basic, it's implemented with an alpha constant and is used directly on the read values from the sensors.
Here is my code:
float[] rotationMatrix = new float[9];
SensorManager.getRotationMatrix(rotationMatrix, null, gravitymeterValues,
magnetometerValues);
float[] remappedRotationMatrix = new float[9];
SensorManager.remapCoordinateSystem(rotationMatrix, SensorManager.AXIS_X,
SensorManager.AXIS_Z, remappedRotationMatrix);
float results[] = new float[3];
SensorManager.getOrientation(remappedRotationMatrix, results);
float azimuth = (float) (results[0] * 180 / Math.PI);
if (azimuth < 0) {
azimuth += 360;
}
float pitch = (float) (results[1] * 180 / Math.PI);
float roll = (float) (results[2] * 180 / Math.PI);
As you see there is no magic here. I call this piece of code when the gravitymeterValues and the magnetometerValues are ready to be used.
My question is how do I stop the azimuth from going crazy when I tilt the phone?
I checked a free app on the Google Play Store, Compass and it hasn't solved this problem, but I hope there is a solution.
I have 2 solutions in mind:
Make the AR view only work in very constrainted pitch angles, right now I have something like pitch >= -5 && pitch <= 30. If this isn't fullfilled the user is shown a screen that asks him/her to rotate the phone to portrait.
Somehow use the pitch to suppress the azimuth, this seems like a pretty device-specific solution though, but of course I'm open for suggestions.
I can also add that I've been searching for a couple of hours for a decent solution and I haven't found any that has given me any better solutions than 2) here.
Thanks in advance!
For complete code see https://github.com/hoananguyen/dsensor
Keep a history and average out, I do not know the correct interpretation of pitch and roll so the following code is for azimuth only.
Class members
private List<float[]> mRotHist = new ArrayList<float[]>();
private int mRotHistIndex;
// Change the value so that the azimuth is stable and fit your requirement
private int mHistoryMaxLength = 40;
float[] mGravity;
float[] mMagnetic;
float[] mRotationMatrix = new float[9];
// the direction of the back camera, only valid if the device is tilted up by
// at least 25 degrees.
private float mFacing = Float.NAN;
public static final float TWENTY_FIVE_DEGREE_IN_RADIAN = 0.436332313f;
public static final float ONE_FIFTY_FIVE_DEGREE_IN_RADIAN = 2.7052603f;
onSensorChanged
#Override
public void onSensorChanged(SensorEvent event)
{
if (event.sensor.getType() == Sensor.TYPE_GRAVITY)
{
mGravity = event.values.clone();
}
else
{
mMagnetic = event.values.clone();
}
if (mGravity != null && mMagnetic != null)
{
if (SensorManager.getRotationMatrix(mRotationMatrix, null, mGravity, mMagnetic))
{
// inclination is the degree of tilt by the device independent of orientation (portrait or landscape)
// if less than 25 or more than 155 degrees the device is considered lying flat
float inclination = (float) Math.acos(mRotationMatrix[8]);
if (inclination < TWENTY_FIVE_DEGREE_IN_RADIAN
|| inclination > ONE_FIFTY_FIVE_DEGREE_IN_RADIAN)
{
// mFacing is undefined, so we need to clear the history
clearRotHist();
mFacing = Float.NaN;
}
else
{
setRotHist();
// mFacing = azimuth is in radian
mFacing = findFacing();
}
}
}
}
private void clearRotHist()
{
if (DEBUG) {Log.d(TAG, "clearRotHist()");}
mRotHist.clear();
mRotHistIndex = 0;
}
private void setRotHist()
{
if (DEBUG) {Log.d(TAG, "setRotHist()");}
float[] hist = mRotationMatrix.clone();
if (mRotHist.size() == mHistoryMaxLength)
{
mRotHist.remove(mRotHistIndex);
}
mRotHist.add(mRotHistIndex++, hist);
mRotHistIndex %= mHistoryMaxLength;
}
private float findFacing()
{
if (DEBUG) {Log.d(TAG, "findFacing()");}
float[] averageRotHist = average(mRotHist);
return (float) Math.atan2(-averageRotHist[2], -averageRotHist[5]);
}
public float[] average(List<float[]> values)
{
float[] result = new float[9];
for (float[] value : values)
{
for (int i = 0; i < 9; i++)
{
result[i] += value[i];
}
}
for (int i = 0; i < 9; i++)
{
result[i] = result[i] / values.size();
}
return result;
}
I'm using android's TYPE_ROTATION_VECTOR sensor to determine rotation around x,y and z and use these measures in my application in a certain way. But when I rotate phone around X axis, roll value (rotation around Y) also changes. And I don't want that. How can I ignore changes in roll when I rotate around X? Here is piece of my code
private final float[] mRotationMatrix = new float[16];
private float[] orientationVals = new float[3];
if (event.sensor.getType() == Sensor.TYPE_ROTATION_VECTOR) {
SensorManager.getRotationMatrixFromVector(
mRotationMatrix , event.values);
SensorManager.getOrientation(mRotationMatrix, orientationVals);
azimuthVal = (Math.round((Math.toDegrees(orientationVals[0]))*100.0)/100.0);
pitchVal= (Math.round((Math.toDegrees(orientationVals[1]))*100.0)/100.0);
rollVal = (Math.round((Math.toDegrees(orientationVals[2]))*100.0)/100.0);
}
In Android, I am using the accelerometer and magnetic field sensor to calculate spatial positioning, as shown in the code below. The getRotationMatrix method generates values that are in real-world units with azimuth, pitch and roll. Azimuth and roll give values in the range of 0 to 180 or 0 to -180. Pitch however gives values from 0 to 90 or 0 to -90. That's a problem for my app because in my app I need to determine unique locations regardless how the device is oriented. With roll, you can have 2 locations with the same value.
I need to apply a matrix transformation that remaps the sensor values to values that range from 0 to 360 degrees (actually, 360 wouldn't be valid since it's the same as 0 and anything close to 360 would result in a number like 359.99999...)
I am not a mathematician and don't know how to use matrixes, let alone use them in Android but I am aware that this is what is required to get the 0 to 360 degree conversion. It would be nice if the matrix also took care of the azimuth and roll as well so that they also produce values from 0 to 360 but if that isn't possible, that's fine since unique positions can still be derived from their sensor values. Any suggestions how how I create this matrix transformation?
#Override
public void onSensorChanged(SensorEvent event)
{
try
{
if (event.sensor.getType() == Sensor.TYPE_ACCELEROMETER)
accelerometerValues = event.values;
else if (event.sensor.getType() == Sensor.TYPE_MAGNETIC_FIELD)
magneticFieldValues = event.values;
if ((accelerometerValues != null) && (magneticFieldValues != null))
{
float[] R = new float[9];
float I[] = new float[9];
boolean success = SensorManager.getRotationMatrix(R, I, accelerometerValues, magneticFieldValues);
if (success)
{
float[] values = new float[3];
SensorManager.getOrientation(R, values);
// Convert from radians to degrees if preferred.
values[0] = (float) Math.toDegrees(values[0]); // Azimuth
values[1] = (float) Math.toDegrees(values[1]); // Pitch
values[2] = (float) Math.toDegrees(values[2]); // Roll
}
}
}
catch (Exception ex)
{
}
}
EDIT:
The raw event values for the pitch do not give you unique values as you rotate the device 360 degrees, so I highly doubt any matrix transformation is going to produce the results I am after. Maybe I am using the wrong sensors.
The Sensor Fusion video looks great, but there's no code:
http://www.youtube.com/watch?v=C7JQ7Rpwn2k&feature=player_detailpage#t=1315s
Here is my code which just uses accelerometer and compass. I also use a Kalman filter on the 3 orientation values, but that's too much code to show here. Ultimately, this works ok, but the result is either too jittery or too laggy depending on what I do with the results and how low I make the filtering factors.
/** Just accelerometer and magnetic sensors */
public abstract class SensorsListener2
implements
SensorEventListener
{
/** The lower this is, the greater the preference which is given to previous values. (slows change) */
private static final float accelFilteringFactor = 0.1f;
private static final float magFilteringFactor = 0.01f;
public abstract boolean getIsLandscape();
#Override
public void onSensorChanged(SensorEvent event) {
Sensor sensor = event.sensor;
int type = sensor.getType();
switch (type) {
case Sensor.TYPE_MAGNETIC_FIELD:
mags[0] = event.values[0] * magFilteringFactor + mags[0] * (1.0f - magFilteringFactor);
mags[1] = event.values[1] * magFilteringFactor + mags[1] * (1.0f - magFilteringFactor);
mags[2] = event.values[2] * magFilteringFactor + mags[2] * (1.0f - magFilteringFactor);
isReady = true;
break;
case Sensor.TYPE_ACCELEROMETER:
accels[0] = event.values[0] * accelFilteringFactor + accels[0] * (1.0f - accelFilteringFactor);
accels[1] = event.values[1] * accelFilteringFactor + accels[1] * (1.0f - accelFilteringFactor);
accels[2] = event.values[2] * accelFilteringFactor + accels[2] * (1.0f - accelFilteringFactor);
break;
default:
return;
}
if(mags != null && accels != null && isReady) {
isReady = false;
SensorManager.getRotationMatrix(rot, inclination, accels, mags);
boolean isLandscape = getIsLandscape();
if(isLandscape) {
outR = rot;
} else {
// Remap the coordinates to work in portrait mode.
SensorManager.remapCoordinateSystem(rot, SensorManager.AXIS_X, SensorManager.AXIS_Z, outR);
}
SensorManager.getOrientation(outR, values);
double x180pi = 180.0 / Math.PI;
float azimuth = (float)(values[0] * x180pi);
float pitch = (float)(values[1] * x180pi);
float roll = (float)(values[2] * x180pi);
// In landscape mode swap pitch and roll and invert the pitch.
if(isLandscape) {
float tmp = pitch;
pitch = -roll;
roll = -tmp;
azimuth = 180 - azimuth;
} else {
pitch = -pitch - 90;
azimuth = 90 - azimuth;
}
onOrientationChanged(azimuth,pitch,roll);
}
}
private float[] mags = new float[3];
private float[] accels = new float[3];
private boolean isReady;
private float[] rot = new float[9];
private float[] outR = new float[9];
private float[] inclination = new float[9];
private float[] values = new float[3];
/**
Azimuth: angle between the magnetic north direction and the Y axis, around the Z axis (0 to 359). 0=North, 90=East, 180=South, 270=West
Pitch: rotation around X axis (-180 to 180), with positive values when the z-axis moves toward the y-axis.
Roll: rotation around Y axis (-90 to 90), with positive values when the x-axis moves toward the z-axis.
*/
public abstract void onOrientationChanged(float azimuth, float pitch, float roll);
}
I tried to figure out how to add gyroscope data, but I am just not doing it right. The google doc at http://developer.android.com/reference/android/hardware/SensorEvent.html shows some code to get a delta matrix from the gyroscope data. The idea seems to be that I'd crank down the filters for the accelerometer and magnetic sensors so that they were really stable. That would keep track of the long term orientation.
Then, I'd keep a history of the most recent N delta matrices from the gyroscope. Each time I got a new one I'd drop off the oldest one and multiply them all together to get a final matrix which I would multiply against the stable matrix returned by the accelerometer and magnetic sensors.
This doesn't seem to work. Or, at least, my implementation of it does not work. The result is far more jittery than just the accelerometer. Increasing the size of the gyroscope history actually increases the jitter which makes me think that I'm not calculating the right values from the gyroscope.
public abstract class SensorsListener3
implements
SensorEventListener
{
/** The lower this is, the greater the preference which is given to previous values. (slows change) */
private static final float kFilteringFactor = 0.001f;
private static final float magKFilteringFactor = 0.001f;
public abstract boolean getIsLandscape();
#Override
public void onSensorChanged(SensorEvent event) {
Sensor sensor = event.sensor;
int type = sensor.getType();
switch (type) {
case Sensor.TYPE_MAGNETIC_FIELD:
mags[0] = event.values[0] * magKFilteringFactor + mags[0] * (1.0f - magKFilteringFactor);
mags[1] = event.values[1] * magKFilteringFactor + mags[1] * (1.0f - magKFilteringFactor);
mags[2] = event.values[2] * magKFilteringFactor + mags[2] * (1.0f - magKFilteringFactor);
isReady = true;
break;
case Sensor.TYPE_ACCELEROMETER:
accels[0] = event.values[0] * kFilteringFactor + accels[0] * (1.0f - kFilteringFactor);
accels[1] = event.values[1] * kFilteringFactor + accels[1] * (1.0f - kFilteringFactor);
accels[2] = event.values[2] * kFilteringFactor + accels[2] * (1.0f - kFilteringFactor);
break;
case Sensor.TYPE_GYROSCOPE:
gyroscopeSensorChanged(event);
break;
default:
return;
}
if(mags != null && accels != null && isReady) {
isReady = false;
SensorManager.getRotationMatrix(rot, inclination, accels, mags);
boolean isLandscape = getIsLandscape();
if(isLandscape) {
outR = rot;
} else {
// Remap the coordinates to work in portrait mode.
SensorManager.remapCoordinateSystem(rot, SensorManager.AXIS_X, SensorManager.AXIS_Z, outR);
}
if(gyroUpdateTime!=0) {
matrixHistory.mult(matrixTmp,matrixResult);
outR = matrixResult;
}
SensorManager.getOrientation(outR, values);
double x180pi = 180.0 / Math.PI;
float azimuth = (float)(values[0] * x180pi);
float pitch = (float)(values[1] * x180pi);
float roll = (float)(values[2] * x180pi);
// In landscape mode swap pitch and roll and invert the pitch.
if(isLandscape) {
float tmp = pitch;
pitch = -roll;
roll = -tmp;
azimuth = 180 - azimuth;
} else {
pitch = -pitch - 90;
azimuth = 90 - azimuth;
}
onOrientationChanged(azimuth,pitch,roll);
}
}
private void gyroscopeSensorChanged(SensorEvent event) {
// This timestep's delta rotation to be multiplied by the current rotation
// after computing it from the gyro sample data.
if(gyroUpdateTime != 0) {
final float dT = (event.timestamp - gyroUpdateTime) * NS2S;
// Axis of the rotation sample, not normalized yet.
float axisX = event.values[0];
float axisY = event.values[1];
float axisZ = event.values[2];
// Calculate the angular speed of the sample
float omegaMagnitude = (float)Math.sqrt(axisX*axisX + axisY*axisY + axisZ*axisZ);
// Normalize the rotation vector if it's big enough to get the axis
if(omegaMagnitude > EPSILON) {
axisX /= omegaMagnitude;
axisY /= omegaMagnitude;
axisZ /= omegaMagnitude;
}
// Integrate around this axis with the angular speed by the timestep
// in order to get a delta rotation from this sample over the timestep
// We will convert this axis-angle representation of the delta rotation
// into a quaternion before turning it into the rotation matrix.
float thetaOverTwo = omegaMagnitude * dT / 2.0f;
float sinThetaOverTwo = (float)Math.sin(thetaOverTwo);
float cosThetaOverTwo = (float)Math.cos(thetaOverTwo);
deltaRotationVector[0] = sinThetaOverTwo * axisX;
deltaRotationVector[1] = sinThetaOverTwo * axisY;
deltaRotationVector[2] = sinThetaOverTwo * axisZ;
deltaRotationVector[3] = cosThetaOverTwo;
}
gyroUpdateTime = event.timestamp;
SensorManager.getRotationMatrixFromVector(deltaRotationMatrix, deltaRotationVector);
// User code should concatenate the delta rotation we computed with the current rotation
// in order to get the updated rotation.
// rotationCurrent = rotationCurrent * deltaRotationMatrix;
matrixHistory.add(deltaRotationMatrix);
}
private float[] mags = new float[3];
private float[] accels = new float[3];
private boolean isReady;
private float[] rot = new float[9];
private float[] outR = new float[9];
private float[] inclination = new float[9];
private float[] values = new float[3];
// gyroscope stuff
private long gyroUpdateTime = 0;
private static final float NS2S = 1.0f / 1000000000.0f;
private float[] deltaRotationMatrix = new float[9];
private final float[] deltaRotationVector = new float[4];
//TODO: I have no idea how small this value should be.
private static final float EPSILON = 0.000001f;
private float[] matrixMult = new float[9];
private MatrixHistory matrixHistory = new MatrixHistory(100);
private float[] matrixTmp = new float[9];
private float[] matrixResult = new float[9];
/**
Azimuth: angle between the magnetic north direction and the Y axis, around the Z axis (0 to 359). 0=North, 90=East, 180=South, 270=West
Pitch: rotation around X axis (-180 to 180), with positive values when the z-axis moves toward the y-axis.
Roll: rotation around Y axis (-90 to 90), with positive values when the x-axis moves toward the z-axis.
*/
public abstract void onOrientationChanged(float azimuth, float pitch, float roll);
}
public class MatrixHistory
{
public MatrixHistory(int size) {
vals = new float[size][];
}
public void add(float[] val) {
synchronized(vals) {
vals[ix] = val;
ix = (ix + 1) % vals.length;
if(ix==0)
full = true;
}
}
public void mult(float[] tmp, float[] output) {
synchronized(vals) {
if(full) {
for(int i=0; i<vals.length; ++i) {
if(i==0) {
System.arraycopy(vals[i],0,output,0,vals[i].length);
} else {
MathUtils.multiplyMatrix3x3(output,vals[i],tmp);
System.arraycopy(tmp,0,output,0,tmp.length);
}
}
} else {
if(ix==0)
return;
for(int i=0; i<ix; ++i) {
if(i==0) {
System.arraycopy(vals[i],0,output,0,vals[i].length);
} else {
MathUtils.multiplyMatrix3x3(output,vals[i],tmp);
System.arraycopy(tmp,0,output,0,tmp.length);
}
}
}
}
}
private int ix = 0;
private boolean full = false;
private float[][] vals;
}
The second block of code contains my changes from the first block of code which add the gyroscope to the mix.
Specifically, the filtering factor for accel is made smaller (making the value more stable). The MatrixHistory class keeps track of the last 100 gyroscope deltaRotationMatrix values which are calculated in the gyroscopeSensorChanged method.
I've seen many questions on this site on this topic. They've helped me get to this point, but I cannot figure out what to do next. I really wish the Sensor Fusion guy had just posted some code somewhere. He obviously had it all put together.
Well, +1 to you for even knowing what a Kalman filter is. If you'd like, I'll edit this post and give you the code I wrote a couple years ago to do what you're trying to do.
But first, I'll tell you why you don't need it.
Modern implementations of the Android sensor stack use Sensor Fusion, as Stan mentioned above. This just means that all of the available data -- accel, mag, gyro -- is collected together in one algorithm, and then all the outputs are read back out in the form of Android sensors.
Edit: I just stumbled on this superb Google Tech Talk on the subject: Sensor Fusion on Android Devices: A Revolution in Motion Processing. Well worth the 45 minutes to watch it if you're interested in the topic.
In essence, Sensor Fusion is a black box. I've looked into the source code of the Android implementation, and it's a big Kalman filter written in C++. Some pretty good code in there, and far more sophisticated than any filter I ever wrote, and probably more sophisticated that what you're writing. Remember, these guys are doing this for a living.
I also know that at least one chipset manufacturer has their own sensor fusion implementation. The manufacturer of the device then chooses between the Android and the vendor implementation based on their own criteria.
Finally, as Stan mentioned above, Invensense has their own sensor fusion implementation at the chip level.
Anyway, what it all boils down to is that the built-in sensor fusion in your device is likely to be superior to anything you or I could cobble together. So what you really want to do is to access that.
In Android, there are both physical and virtual sensors. The virtual sensors are the ones that are synthesized from the available physical sensors. The best-known example is TYPE_ORIENTATION which takes accelerometer and magnetometer and creates roll/pitch/heading output. (By the way, you should not use this sensor; it has too many limitations.)
But the important thing is that newer versions of Android contain these two new virtual sensors:
TYPE_GRAVITY is the accelerometer input with the effect of motion filtered out
TYPE_LINEAR_ACCELERATION is the accelerometer with the gravity component filtered out.
These two virtual sensors are synthesized through a combination of accelerometer input and gyro input.
Another notable sensor is TYPE_ROTATION_VECTOR which is a Quaternion synthesized from accelerometer, magnetometer, and gyro. It represents the full 3-d orientation of the device with the effects of linear acceleration filtered out.
However, Quaternions are a little bit abstract for most people, and since you're likely working with 3-d transformations anyway, your best approach is to combine TYPE_GRAVITY and TYPE_MAGNETIC_FIELD via SensorManager.getRotationMatrix().
One more point: if you're working with a device running an older version of Android, you need to detect that you're not receiving TYPE_GRAVITY events and use TYPE_ACCELEROMETER instead. Theoretically, this would be a place to use your own kalman filter, but if your device doesn't have sensor fusion built in, it probably doesn't have gyros either.
Anyway, here's some sample code to show how I do it.
// Requires 1.5 or above
class Foo extends Activity implements SensorEventListener {
SensorManager sensorManager;
float[] gData = new float[3]; // Gravity or accelerometer
float[] mData = new float[3]; // Magnetometer
float[] orientation = new float[3];
float[] Rmat = new float[9];
float[] R2 = new float[9];
float[] Imat = new float[9];
boolean haveGrav = false;
boolean haveAccel = false;
boolean haveMag = false;
onCreate() {
// Get the sensor manager from system services
sensorManager =
(SensorManager)getSystemService(Context.SENSOR_SERVICE);
}
onResume() {
super.onResume();
// Register our listeners
Sensor gsensor = sensorManager.getDefaultSensor(Sensor.TYPE_GRAVITY);
Sensor asensor = sensorManager.getDefaultSensor(Sensor.TYPE_ACCELEROMETER);
Sensor msensor = sensorManager.getDefaultSensor(Sensor.TYPE_MAGNETIC_FIELD);
sensorManager.registerListener(this, gsensor, SensorManager.SENSOR_DELAY_GAME);
sensorManager.registerListener(this, asensor, SensorManager.SENSOR_DELAY_GAME);
sensorManager.registerListener(this, msensor, SensorManager.SENSOR_DELAY_GAME);
}
public void onSensorChanged(SensorEvent event) {
float[] data;
switch( event.sensor.getType() ) {
case Sensor.TYPE_GRAVITY:
gData[0] = event.values[0];
gData[1] = event.values[1];
gData[2] = event.values[2];
haveGrav = true;
break;
case Sensor.TYPE_ACCELEROMETER:
if (haveGrav) break; // don't need it, we have better
gData[0] = event.values[0];
gData[1] = event.values[1];
gData[2] = event.values[2];
haveAccel = true;
break;
case Sensor.TYPE_MAGNETIC_FIELD:
mData[0] = event.values[0];
mData[1] = event.values[1];
mData[2] = event.values[2];
haveMag = true;
break;
default:
return;
}
if ((haveGrav || haveAccel) && haveMag) {
SensorManager.getRotationMatrix(Rmat, Imat, gData, mData);
SensorManager.remapCoordinateSystem(Rmat,
SensorManager.AXIS_Y, SensorManager.AXIS_MINUS_X, R2);
// Orientation isn't as useful as a rotation matrix, but
// we'll show it here anyway.
SensorManager.getOrientation(R2, orientation);
float incl = SensorManager.getInclination(Imat);
Log.d(TAG, "mh: " + (int)(orientation[0]*DEG));
Log.d(TAG, "pitch: " + (int)(orientation[1]*DEG));
Log.d(TAG, "roll: " + (int)(orientation[2]*DEG));
Log.d(TAG, "yaw: " + (int)(orientation[0]*DEG));
Log.d(TAG, "inclination: " + (int)(incl*DEG));
}
}
}
Hmmm; if you happen to have a Quaternion library handy, it's probably simpler just to receive TYPE_ROTATION_VECTOR and convert that to an array.
To the question where to find complete code, here's a default implementation on Android jelly bean: https://android.googlesource.com/platform/frameworks/base/+/jb-release/services/sensorservice/
Start by checking the fusion.cpp/h.
It uses Modified Rodrigues Parameters (close to Euler angles) instead of quaternions. In addition to orientation the Kalman filter estimates gyro drift. For measurement updates it uses magnetometer and, a bit incorrectly, acceleration (specific force).
To make use of the code you should either be a wizard or know the basics of INS and KF. Many parameters have to be fine-tuned for the filter to work. As Edward adequately put, these guys are doing this for living.
At least in google's galaxy nexus this default implementation is left unused and is overridden by Invense's proprietary system.