I am trying to figure out how to point the device marker where the user is heading. I tried the code below to get the bearing angle between the true north and where the user is heading. Once the bearing is found, I would apply it to the device marker and rotate it based on that bearing, but it only works when the device is not moving.
I tried out the code I found from the link below.
Code Source Link
// raw inputs from Android sensors
float m_Norm_Gravity; // length of raw gravity vector received in onSensorChanged(...). NB: should be about 10
float[] m_NormGravityVector = m_NormMagFieldValues = null;; // Normalised gravity vector, (i.e. length of this vector is 1), which points straight up into space
float m_Norm_MagField; // length of raw magnetic field vector received in onSensorChanged(...).
float[] m_NormMagFieldValues; // Normalised magnetic field vector, (i.e. length of this vector is 1)
// accuracy specifications. SENSOR_UNAVAILABLE if unknown, otherwise SensorManager.SENSOR_STATUS_UNRELIABLE, SENSOR_STATUS_ACCURACY_LOW, SENSOR_STATUS_ACCURACY_MEDIUM or SENSOR_STATUS_ACCURACY_HIGH
int m_GravityAccuracy; // accuracy of gravity sensor
int m_MagneticFieldAccuracy; // accuracy of magnetic field sensor
// values calculated once gravity and magnetic field vectors are available
float[] m_NormEastVector = new float[3]; // normalised cross product of raw gravity vector with magnetic field values, points east
float[] m_NormNorthVector = new float[3]; // Normalised vector pointing to magnetic north
boolean m_OrientationOK = false; // set true if m_azimuth_radians and m_pitch_radians have successfully been calculated following a call to onSensorChanged(...)
float m_azimuth_radians; // angle of the device from magnetic north
float m_pitch_radians; // tilt angle of the device from the horizontal. m_pitch_radians = 0 if the device if flat, m_pitch_radians = Math.PI/2 means the device is upright.
float m_pitch_axis_radians; // angle which defines the axis for the rotation m_pitch_radians
#Override
public void onSensorChanged(SensorEvent sensorEvent) {
int SensorType = sensorEvent.sensor.getType();
switch(SensorType) {
case Sensor.TYPE_GRAVITY:
if (m_NormGravityVector == null) m_NormGravityVector = new float[3];
System.arraycopy(sensorEvent.values, 0, m_NormGravityVector, 0, m_NormGravityVector.length);
m_Norm_Gravity = (float)Math.sqrt(m_NormGravityVector[0]*m_NormGravityVector[0] + m_NormGravityVector[1]*m_NormGravityVector[1] + m_NormGravityVector[2]*m_NormGravityVector[2]);
for(int i=0; i < m_NormGravityVector.length; i++) m_NormGravityVector[i] /= m_Norm_Gravity;
break;
case Sensor.TYPE_MAGNETIC_FIELD:
if (m_NormMagFieldValues == null) m_NormMagFieldValues = new float[3];
System.arraycopy(sensorEvent.values, 0, m_NormMagFieldValues, 0, m_NormMagFieldValues.length);
m_Norm_MagField = (float)Math.sqrt(m_NormMagFieldValues[0]*m_NormMagFieldValues[0] + m_NormMagFieldValues[1]*m_NormMagFieldValues[1] + m_NormMagFieldValues[2]*m_NormMagFieldValues[2]);
for(int i=0; i < m_NormMagFieldValues.length; i++) m_NormMagFieldValues[i] /= m_Norm_MagField;
break;
}
if (m_NormGravityVector != null && m_NormMagFieldValues != null) {
// first calculate the horizontal vector that points due east
float East_x = m_NormMagFieldValues[1] * m_NormGravityVector[2] - m_NormMagFieldValues[2] * m_NormGravityVector[1];
float East_y = m_NormMagFieldValues[2] * m_NormGravityVector[0] - m_NormMagFieldValues[0] * m_NormGravityVector[2];
float East_z = m_NormMagFieldValues[0] * m_NormGravityVector[1] - m_NormMagFieldValues[1] * m_NormGravityVector[0];
float norm_East = (float) Math.sqrt(East_x * East_x + East_y * East_y + East_z * East_z);
if (m_Norm_Gravity * m_Norm_MagField * norm_East < 0.1f) { // Typical values are > 100.
m_OrientationOK = false; // device is close to free fall (or in space?), or close to magnetic north pole.
} else {
m_NormEastVector[0] = East_x / norm_East;
m_NormEastVector[1] = East_y / norm_East;
m_NormEastVector[2] = East_z / norm_East;
// next calculate the horizontal vector that points due north
float M_dot_G = (m_NormGravityVector[0] * m_NormMagFieldValues[0] + m_NormGravityVector[1] * m_NormMagFieldValues[1] + m_NormGravityVector[2] * m_NormMagFieldValues[2]);
float North_x = m_NormMagFieldValues[0] - m_NormGravityVector[0] * M_dot_G;
float North_y = m_NormMagFieldValues[1] - m_NormGravityVector[1] * M_dot_G;
float North_z = m_NormMagFieldValues[2] - m_NormGravityVector[2] * M_dot_G;
float norm_North = (float) Math.sqrt(North_x * North_x + North_y * North_y + North_z * North_z);
m_NormNorthVector[0] = North_x / norm_North;
m_NormNorthVector[1] = North_y / norm_North;
m_NormNorthVector[2] = North_z / norm_North;
// take account of screen rotation away from its natural rotation
int rotation = getActivity().getWindowManager().getDefaultDisplay().getRotation();
float screen_adjustment = 0;
switch (rotation) {
case Surface.ROTATION_0:
screen_adjustment = 0;
break;
case Surface.ROTATION_90:
screen_adjustment = (float) Math.PI / 2;
break;
case Surface.ROTATION_180:
screen_adjustment = (float) Math.PI;
break;
case Surface.ROTATION_270:
screen_adjustment = 3 * (float) Math.PI / 2;
break;
}
// NB: the rotation matrix has now effectively been calculated. It consists of the three vectors m_NormEastVector[], m_NormNorthVector[] and m_NormGravityVector[]
// calculate all the required angles from the rotation matrix
// NB: see https://math.stackexchange.com/questions/381649/whats-the-best-3d-angular-co-ordinate-system-for-working-with-smartfone-apps
float sin = m_NormEastVector[1] - m_NormNorthVector[0], cos = m_NormEastVector[0] + m_NormNorthVector[1];
m_azimuth_radians = (float) (sin != 0 && cos != 0 ? Math.atan2(sin, cos) : 0);
m_pitch_radians = (float) Math.acos(m_NormGravityVector[2]);
sin = -m_NormEastVector[1] - m_NormNorthVector[0];
cos = m_NormEastVector[0] - m_NormNorthVector[1];
float aximuth_plus_two_pitch_axis_radians = (float) (sin != 0 && cos != 0 ? Math.atan2(sin, cos) : 0);
m_pitch_axis_radians = (float) (aximuth_plus_two_pitch_axis_radians - m_azimuth_radians) / 2;
m_azimuth_radians += screen_adjustment;
m_pitch_axis_radians += screen_adjustment;
m_OrientationOK = true;
currentAzimuth = (float) Math.toDegrees(m_azimuth_radians);
}
}
}
#Override
public void onAccuracyChanged(Sensor sensor, int accuracy) {
int SensorType = sensor.getType();
switch(SensorType) {
case Sensor.TYPE_GRAVITY: m_GravityAccuracy = accuracy; break;
case Sensor.TYPE_MAGNETIC_FIELD: m_MagneticFieldAccuracy = accuracy; break;
}
}
I'm making program for showing objects from map on camera and this works almost well except few degrees to left and right from vertical orientation (like in 80-110 and 260-280 degrees). In other +-320 degrees it works well. I've tried to use TYPE_ROTATION_VECTOR and accelerometer with magnetometer and they have the same result. Does anybody know any solution?
with TYPE_ROTATION_VECTOR:
if (event.sensor.getType() == Sensor.TYPE_ROTATION_VECTOR)
{
float[] roationV = new float[16];
SensorManager.getRotationMatrixFromVector(roationV, event.values);
float[] orientationValuesV = new float[3];
SensorManager.getOrientation(roationV, orientationValuesV);
tvHeading.setText(String.format(
"Coordinates: lat = %1$.2f, lon = %2$.2f, time = %3$.2f",
orientationValuesV[0], orientationValuesV[1], orientationValuesV[2]));
float[] rotationMatrix=new float[16];
mSensorManager.getRotationMatrixFromVector(rotationMatrix, event.values);
float[] orientationValues = new float[3];
SensorManager.getOrientation(rotationMatrix, orientationValues);
double azimuth = Math.toDegrees(orientationValues[0]);
double pitch = Math.toDegrees(orientationValues[1]);
double roll = Math.toDegrees(orientationValues[2]);
tvOrientation.setText(String.format(
"Coordinates: lat = %1$.2f, lon = %2$.2f, time = %3$.2f",
azimuth,pitch,roll));
}
with accelerometer+magnetometer
if (event.sensor == mAccelerometer) {
System.arraycopy(event.values, 0, mLastAccelerometer, 0, event.values.length);
mLastAccelerometer = meanFilterAccelSmoothing
.addSamples(mLastAccelerometer);
mLastAccelerometer = medianFilterAccelSmoothing
.addSamples(mLastAccelerometer);
for (int i = 0; i < mLastAccelerometer.length; i++) {
mLastAccelerometer[i] = (float) Math.floor(mLastAccelerometer[i] * 1000) / 1000;
}
mLastAccelerometerSet = true;
}
if (event.sensor == mMagnetometer) {
System.arraycopy(event.values, 0, mLastMagnetometer, 0, event.values.length);
mLastMagnetometer = meanFilterMagneticSmoothing.addSamples(mLastMagnetometer);
mLastMagnetometer = medianFilterMagneticSmoothing.addSamples(mLastMagnetometer);
for (int i = 0; i < mLastMagnetometer.length; i++) {
mLastMagnetometer[i] = (float) Math.floor(mLastMagnetometer[i] * 1000) / 1000;
}
mLastMagnetometerSet = true;
}
if (mLastAccelerometerSet && mLastMagnetometerSet) {
SensorManager.getRotationMatrix(mR, null, mLastAccelerometer, mLastMagnetometer);
SensorManager.getOrientation(mR, mOrientation);
if (angeles.size() > 0) {
for (int i = 0; i < mapObjects.size(); i++) {
compassFunc(i, mOrientation[0], mOrientation[1], mOrientation[2]);
}
}
private void compassFunc(int number, float... values) {
double angularXSpeed = Math.floor(values[0] * 180 / Math.PI * 100) / 100;
double angularYSpeed = Math.floor(values[1] * 180 / Math.PI * 100) / 100;
double angularZSpeed = Math.floor(values[2] * 180 / Math.PI * 100) / 100;
tvOrientation.setText(String.format(
"Screen: lt= %1$.2f : %2$.2f,rt= %3$.2f : %4$.2f,lb= %5$.2f : %6$.2f,rb= %7$.2f : %8$.2f",
xLeftTop, yLeftTop, xRightTop,yRightTop,xLeftBottom,yLeftBottom,xRightBottom,yRightBottom));
}
This sounds like a typical case of Gimbal Lock. Your description of how rotation around one axis acts up when another reaches +-90 degrees suggests that this is indeed the case.
This is a fundamental problem with Euler angles (Yaw/Azimuth, Pitch, Roll), which is why most such computations are done using rotation matrices or quaternions, and Euler angles most often only are used when a particular orientation is to be displayed to a human (humans are generally bad at interpreting rotation matrices and quaternions).
The ROTATION_VECTOR sensor outputs it's data in a quaternion format (source), albeit with rearranged values, and the getRotationMatrixFromVector() method turns this into a rotation matrix. I would suggest using one of these descriptions for your internal calculations.
The answers to this similar question provide some concrete suggestions on how to solve the issue.
I am trying to create a simple compass app (for learning purposes) and I am using the rotation matrix using the magnetic sensor and accelerometer. This is my code:
#Override
public void onSensorChanged(SensorEvent event) {
if (event.sensor.getType() == Sensor.TYPE_MAGNETIC_FIELD) {
geomagnetic[0] = event.values[0];
geomagnetic[1] = event.values[1];
geomagnetic[2] = event.values[2];
havemag = true;
}
if (event.sensor.getType() == Sensor.TYPE_ACCELEROMETER) {
gravity[0] = event.values[0];
gravity[1] = event.values[1];
gravity[2] = event.values[2];
haveacc = true;
}
if (haveacc && havemag) {
if (SensorManager.getRotationMatrix(Rm, I, gravity, geomagnetic)) {
float[] result = new float[3];
SensorManager.getOrientation(Rm, result);
azimuth = result[0];
pitch = result[1];
roll = result[2];
}
}
}
When I look at these values while moving my phone though (outputing using Log.d), I get incorrect values. I use this code to make degrees out of them:
int deg = (int)(azimuth * (float)57.295);
But this value never reaches zero (which is supposed to be on the exact opposite side of 180/-180). Instead, the opposite of where 180/-180 is, I get -85 (approx).
You should use the proper JavaMath function to convert your radian angle to degree. Furthermore converting the angle (in degree) to a [0 - 360] degree interval is very useful.
// angle in degree [-180 - 0 - 180] degree
azimuth = Math.toDegrees( result[0] )
// angle in degree [0 - 360] degree
azimuth = ( Math.toDegrees( result[0] ) + 360 ) % 360;
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.