Sensor Fusion 视频看起来很棒，但没有代码： http ://www.youtube.com/watch?v=C7JQ7Rpwn2k&feature=player_detailpage#t=1315s
这是我的代码，它只使用加速度计和指南针。我还在 3 个方向值上使用了卡尔曼滤波器，但是这里显示的代码太多了。最终，这可以正常工作，但结果要么过于紧张，要么过于滞后，这取决于我对结果的处理方式以及我将过滤因子设为多低。
/** 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);
}
我试图弄清楚如何添加陀螺仪数据，但我做得不对。http://developer.android.com/reference/android/hardware/SensorEvent.html上的谷歌文档显示了一些从陀螺仪数据中获取增量矩阵的代码。这个想法似乎是我将加速计和磁传感器的滤波器调低，以使它们真正稳定。这将跟踪长期方向。
然后，我会保留来自陀螺仪的最新 N delta 矩阵的历史记录。每次我得到一个新的时，我都会放弃最旧的一个，然后将它们全部相乘以获得最终矩阵，然后将其与加速度计和磁传感器返回的稳定矩阵相乘。
这似乎不起作用。或者，至少，我的实现不起作用。结果远比加速度计更加紧张。增加陀螺仪历史的大小实际上会增加抖动，这让我认为我没有从陀螺仪计算正确的值。
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;
}
第二个代码块包含我从第一个代码块中添加陀螺仪的更改。
具体来说，加速度的过滤因子变小（使值更稳定）。MatrixHistory 类跟踪在 gyroscopeSensorChanged 方法中计算的最后 100 个陀螺仪 deltaRotationMatrix 值。
我在这个网站上看到了很多关于这个主题的问题。他们帮助我达到了这一点，但我不知道下一步该做什么。我真希望 Sensor Fusion 的人刚刚在某处发布了一些代码。显然，他把这一切都放在了一起。